diff --git a/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2 b/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2
index 8d59f26c17f5b79eef8f33cf16528b0e4000d161..da8080aaa60f596fd376d0221fb6993eba14d8bc 100755
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diff --git a/data/1500iters.png b/data/1500iters.png
new file mode 100644
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Binary files /dev/null and b/data/1500iters.png differ
diff --git a/dual.txt b/dual.txt
index f824c8055e1ee3b4703c99bbdc9734a885554b0a..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
--- a/dual.txt
+++ b/dual.txt
@@ -1,351 +0,0 @@
-objective value:                                    2
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 86.815897	objective value:                               -52440
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 70.915443	objective value:                               -72380
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 61.473572	objective value:                               -84998
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 55.108983	objective value:                              -101690
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 50.289164	objective value:                              -110880
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 46.603188	objective value:                              -119066
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 43.726422	objective value:                              -129901
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 41.178473	objective value:                              -136241
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 39.102430	objective value:                              -142151
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 37.425077	objective value:                              -150107
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 35.763109	objective value:                              -154911
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 34.392530	objective value:                              -159531
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 33.293071	objective value:                              -165805
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 32.078030	objective value:                              -169663
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 31.088583	objective value:                              -173458
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 30.320833	objective value:                              -178633
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 29.365513	objective value:                    -181872.960784314
-t_x_{1}_{0}                                         1 	(obj:51.0196078431372)
-t_x_{2}_{0}                                         1 	(obj:49.0196078431372)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 100.039216, lowerbound=1.000000, norm of subgrad 28.748455	objective value:                              -186587
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 27.910571	objective value:                              -189604
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{15}                                        1 	(obj:30)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 60.000000, lowerbound=30.000000, norm of subgrad 27.263267	objective value:                              -192431
-t_x_{1}_{0}                                         1 	(obj:2)
-t_x_{2}_{0}                                         1 	(obj:0)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 2.000000, lowerbound=1.000000, norm of subgrad 26.810785	objective value:                              -196367
-t_x_{1}_{30}                                        1 	(obj:60)
-t_x_{2}_{30}                                        1 	(obj:60)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 120.000000, lowerbound=60.000000, norm of subgrad 26.099309	objective value:                    -199005.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 25.612497	objective value:                    -201505.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 25.119713	objective value:                    -204478.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 24.656096	objective value:                    -207362.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 24.218909	objective value:                    -210140.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 23.805762	objective value:                    -212819.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 23.414555	objective value:                    -215404.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 23.043437	objective value:                    -217904.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 22.690768	objective value:                    -220324.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 22.355089	objective value:                    -222668.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 22.035096	objective value:                    -224940.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 21.729622	objective value:                    -227146.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 21.437617	objective value:                    -229289.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 21.158135	objective value:                    -231373.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 20.890318	objective value:                    -233399.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 20.633391	objective value:                    -235374.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 20.386647	objective value:                    -237296.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 20.149442	objective value:                    -239171.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 19.921186	objective value:                    -241000.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 19.701342	objective value:                    -242786.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 19.489413	objective value:                    -244530.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 19.284945	objective value:                    -246235.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 19.087518	objective value:                    -247901.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.896745	objective value:                    -249532.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.712267	objective value:                    -251128.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.533753	objective value:                    -252690.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.360895	objective value:                    -254221.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.193405	objective value:                    -255721.355731225
-t_x_{1}_{15}                                        1 	(obj:30)
-t_x_{2}_{0}                                         1 	(obj:29.6442687747036)
-t_x_{3}_{0}                                         1 	(obj:0)
-t_x_{4}_{0}                                         1 	(obj:0)
-t_x_{5}_{0}                                         1 	(obj:0)
-t_x_{6}_{0}                                         1 	(obj:0)
-dualbound = 59.644269, lowerbound=15.000000, norm of subgrad 18.031019	
\ No newline at end of file
diff --git a/first.ipynb b/first.ipynb
index ad6adc5dbf62fd4d43a87ffb76c42039bfc4a6d0..28d54ea7745f1796d86353d2481c8273c6c5fc58 100644
--- a/first.ipynb
+++ b/first.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,17 +20,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "150\n",
-      "\n",
-      "[6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 48.0, 48.0, 6.0, 48.0, 48.0, 48.0, 6.0, 76.0, 48.0, 6.0, 76.0, 34.0, 76.0, 6.0, 76.0, 6.0, 76.0, 34.0, 34.0, 62.0, 6.0, 76.0, 34.0, 62.0, 6.0, 76.0, 34.0, 62.0, 6.0, 76.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0, 91.0, 64.0]\n",
-      "91.0\n"
+      "11\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-30-82752eb5f629>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: "
      ]
     }
    ],
@@ -55,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -84,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +102,7 @@
        "(125, 1)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -115,13 +124,13 @@
        "Text(0.5, 1.0, '6flights, four sectors, opt using lagrangian r.')"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -145,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -190,7 +199,7 @@
        "13"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -210,7 +219,7 @@
        "Text(0.5, 1.0, 'LP-Relaxation problem_t_pricer_0_2.lp')"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -237,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -287,10 +296,10 @@
  ],
  "metadata": {
   "interpreter": {
-   "hash": "ca2525eab514af7f1335f4a0de66f0c96f9a7997643233c6c1749b1bac977781"
+   "hash": "38b34d0cb5915ebd706651697a9bad136b66d87bcc8c7f5d873fb1545f3d61bf"
   },
   "kernelspec": {
-   "display_name": "Python 3.7.10 64-bit ('base': conda)",
+   "display_name": "Python 3.7.11 64-bit ('scip': conda)",
    "name": "python3"
   },
   "language_info": {
@@ -303,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.7.11"
   }
  },
  "nbformat": 4,
diff --git a/iter.txt b/iter.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lowerbounds.txt b/lowerbounds.txt
index e66bff0dc68bfc51d3f9df4f00dbe963f7a8ba81..2aa87b782f9b97b49f75845ae77419d8fb71d0e5 100644
--- a/lowerbounds.txt
+++ b/lowerbounds.txt
@@ -1,51 +1,651 @@
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diff --git a/obj/static/O.linux.x86_64.gnu.opt/relax_lagr.o b/obj/static/O.linux.x86_64.gnu.opt/relax_lagr.o
index d989a4604f982bc5272425f2402c0cf329a70533..6db3b4a4addd7da63c59396f073ba13656d41f12 100644
Binary files a/obj/static/O.linux.x86_64.gnu.opt/relax_lagr.o and b/obj/static/O.linux.x86_64.gnu.opt/relax_lagr.o differ
diff --git a/sol.txt b/sol.txt
index ae678f3a999b0eb8291bbfc3e29e160cf5f249a4..d99c9014604eb0b877903d0b144a778b3a8d2c54 100644
--- a/sol.txt
+++ b/sol.txt
@@ -1,101 +1,651 @@
-number of solutions 1, first iteration 	 bound=2.000000, 	 objsol=2.000000 
-lowerbound = 1.000000 
- number of solutions 2, first iteration 	 bound=-52440.000000, 	 objsol=-52440.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-72380.000000, 	 objsol=-72380.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-84998.000000, 	 objsol=-84998.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-101690.000000, 	 objsol=-101690.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-110880.000000, 	 objsol=-110880.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-119066.000000, 	 objsol=-119066.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-129901.000000, 	 objsol=-129901.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-136241.000000, 	 objsol=-136241.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-142151.000000, 	 objsol=-142151.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-150107.000000, 	 objsol=-150107.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-154911.000000, 	 objsol=-154911.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-159531.000000, 	 objsol=-159531.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-165805.000000, 	 objsol=-165805.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-169663.000000, 	 objsol=-169663.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-173458.000000, 	 objsol=-173458.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-178633.000000, 	 objsol=-178633.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-181872.960784, 	 objsol=-181872.960784 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-186587.000000, 	 objsol=-186587.000000 
-lowerbound = 60.000000 
- number of solutions 3, first iteration 	 bound=-189604.000000, 	 objsol=-189604.000000 
-lowerbound = 30.000000 
- number of solutions 3, first iteration 	 bound=-192431.000000, 	 objsol=-192431.000000 
-lowerbound = 1.000000 
- number of solutions 3, first iteration 	 bound=-196367.000000, 	 objsol=-196367.000000 
-lowerbound = 60.000000 
- number of solutions 4, first iteration 	 bound=-199005.355731, 	 objsol=-199005.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-201505.355731, 	 objsol=-201505.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-204478.355731, 	 objsol=-204478.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-207362.355731, 	 objsol=-207362.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-210140.355731, 	 objsol=-210140.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-212819.355731, 	 objsol=-212819.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-215404.355731, 	 objsol=-215404.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-217904.355731, 	 objsol=-217904.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-220324.355731, 	 objsol=-220324.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-222668.355731, 	 objsol=-222668.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-224940.355731, 	 objsol=-224940.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-227146.355731, 	 objsol=-227146.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-229289.355731, 	 objsol=-229289.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-231373.355731, 	 objsol=-231373.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-233399.355731, 	 objsol=-233399.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-235374.355731, 	 objsol=-235374.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-237296.355731, 	 objsol=-237296.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-239171.355731, 	 objsol=-239171.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-241000.355731, 	 objsol=-241000.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-242786.355731, 	 objsol=-242786.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-244530.355731, 	 objsol=-244530.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-246235.355731, 	 objsol=-246235.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-247901.355731, 	 objsol=-247901.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-249532.355731, 	 objsol=-249532.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-251128.355731, 	 objsol=-251128.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-252690.355731, 	 objsol=-252690.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-254221.355731, 	 objsol=-254221.355731 
-lowerbound = 15.000000 
- number of solutions 4, first iteration 	 bound=-255721.355731, 	 objsol=-255721.355731 
-lowerbound = 15.000000 
- 
\ No newline at end of file
+number of solutions 1, first iteration 	 bound=969553.451655, 	 objsol=969553.451655 
+number of solutions 2, first iteration 	 bound=911613.111366, 	 objsol=911613.111366 
+number of solutions 4, first iteration 	 bound=947154.171486, 	 objsol=947154.171486 
+number of solutions 5, first iteration 	 bound=943263.213453, 	 objsol=943263.213453 
+number of solutions 6, first iteration 	 bound=961592.718970, 	 objsol=961592.718970 
+number of solutions 7, first iteration 	 bound=975146.613930, 	 objsol=975146.613930 
+number of solutions 7, first iteration 	 bound=989344.620330, 	 objsol=989344.620330 
+number of solutions 8, first iteration 	 bound=999024.176262, 	 objsol=999024.176262 
+number of solutions 9, first iteration 	 bound=1008350.920374, 	 objsol=1008350.920374 
+number of solutions 11, first iteration 	 bound=1020026.835179, 	 objsol=1020026.835179 
+number of solutions 11, first iteration 	 bound=1023789.204847, 	 objsol=1023789.204847 
+number of solutions 11, first iteration 	 bound=1037572.779662, 	 objsol=1037572.779662 
+number of solutions 11, first iteration 	 bound=1043550.340114, 	 objsol=1043550.340114 
+number of solutions 11, first iteration 	 bound=1051710.589431, 	 objsol=1051710.589431 
+number of solutions 10, first iteration 	 bound=1058427.147271, 	 objsol=1058427.147271 
+number of solutions 12, first iteration 	 bound=1063293.445488, 	 objsol=1063293.445488 
+number of solutions 10, first iteration 	 bound=1072473.640624, 	 objsol=1072473.640624 
+number of solutions 12, first iteration 	 bound=1076886.141898, 	 objsol=1076886.141898 
+number of solutions 10, first iteration 	 bound=1086729.286001, 	 objsol=1086729.286001 
+number of solutions 12, first iteration 	 bound=1091780.455877, 	 objsol=1091780.455877 
+number of solutions 10, first iteration 	 bound=1094896.809334, 	 objsol=1094896.809334 
+number of solutions 11, first iteration 	 bound=1101263.149421, 	 objsol=1101263.149421 
+number of solutions 12, first iteration 	 bound=1103431.934686, 	 objsol=1103431.934686 
+number of solutions 11, first iteration 	 bound=1111036.222705, 	 objsol=1111036.222705 
+number of solutions 11, first iteration 	 bound=1115860.276809, 	 objsol=1115860.276809 
+number of solutions 11, first iteration 	 bound=1119922.138092, 	 objsol=1119922.138092 
+number of solutions 11, first iteration 	 bound=1124960.296185, 	 objsol=1124960.296185 
+number of solutions 11, first iteration 	 bound=1132123.876662, 	 objsol=1132123.876662 
+number of solutions 11, first iteration 	 bound=1133652.768532, 	 objsol=1133652.768532 
+number of solutions 11, first iteration 	 bound=1136144.901815, 	 objsol=1136144.901815 
+number of solutions 11, first iteration 	 bound=1141097.358296, 	 objsol=1141097.358296 
+number of solutions 11, first iteration 	 bound=1143101.259766, 	 objsol=1143101.259766 
+number of solutions 10, first iteration 	 bound=1148002.683780, 	 objsol=1148002.683780 
+number of solutions 12, first iteration 	 bound=1153159.516056, 	 objsol=1153159.516056 
+number of solutions 11, first iteration 	 bound=1153338.723749, 	 objsol=1153338.723749 
+number of solutions 11, first iteration 	 bound=1158674.315675, 	 objsol=1158674.315675 
+number of solutions 11, first iteration 	 bound=1162297.618028, 	 objsol=1162297.618028 
+number of solutions 11, first iteration 	 bound=1164160.056187, 	 objsol=1164160.056187 
+number of solutions 11, first iteration 	 bound=1168498.610213, 	 objsol=1168498.610213 
+number of solutions 10, first iteration 	 bound=1169729.460704, 	 objsol=1169729.460704 
+number of solutions 11, first iteration 	 bound=1173952.832273, 	 objsol=1173952.832273 
+number of solutions 12, first iteration 	 bound=1178270.501656, 	 objsol=1178270.501656 
+number of solutions 11, first iteration 	 bound=1178878.876044, 	 objsol=1178878.876044 
+number of solutions 11, first iteration 	 bound=1181975.581725, 	 objsol=1181975.581725 
+number of solutions 10, first iteration 	 bound=1184448.929978, 	 objsol=1184448.929978 
+number of solutions 11, first iteration 	 bound=1188609.544868, 	 objsol=1188609.544868 
+number of solutions 12, first iteration 	 bound=1188830.345998, 	 objsol=1188830.345998 
+number of solutions 10, first iteration 	 bound=1194249.329747, 	 objsol=1194249.329747 
+number of solutions 12, first iteration 	 bound=1196132.545007, 	 objsol=1196132.545007 
+number of solutions 10, first iteration 	 bound=1197416.779761, 	 objsol=1197416.779761 
+number of solutions 12, first iteration 	 bound=1198769.130753, 	 objsol=1198769.130753 
+number of solutions 11, first iteration 	 bound=1203012.987614, 	 objsol=1203012.987614 
+number of solutions 11, first iteration 	 bound=1204208.203188, 	 objsol=1204208.203188 
+number of solutions 10, first iteration 	 bound=1205848.372438, 	 objsol=1205848.372438 
+number of solutions 12, first iteration 	 bound=1210379.200196, 	 objsol=1210379.200196 
+number of solutions 11, first iteration 	 bound=1210910.524724, 	 objsol=1210910.524724 
+number of solutions 11, first iteration 	 bound=1212408.050573, 	 objsol=1212408.050573 
+number of solutions 11, first iteration 	 bound=1215354.767561, 	 objsol=1215354.767561 
+number of solutions 11, first iteration 	 bound=1214580.986949, 	 objsol=1214580.986949 
+number of solutions 10, first iteration 	 bound=1221664.782381, 	 objsol=1221664.782381 
+number of solutions 12, first iteration 	 bound=1220831.170901, 	 objsol=1220831.170901 
+number of solutions 11, first iteration 	 bound=1219416.355717, 	 objsol=1219416.355717 
+number of solutions 11, first iteration 	 bound=1227249.575602, 	 objsol=1227249.575602 
+number of solutions 10, first iteration 	 bound=1224533.491777, 	 objsol=1224533.491777 
+number of solutions 11, first iteration 	 bound=1228388.071534, 	 objsol=1228388.071534 
+number of solutions 12, first iteration 	 bound=1229978.533500, 	 objsol=1229978.533500 
+number of solutions 10, first iteration 	 bound=1231536.910022, 	 objsol=1231536.910022 
+number of solutions 12, first iteration 	 bound=1233839.968237, 	 objsol=1233839.968237 
+number of solutions 10, first iteration 	 bound=1235535.554008, 	 objsol=1235535.554008 
+number of solutions 12, first iteration 	 bound=1233975.317661, 	 objsol=1233975.317661 
+number of solutions 10, first iteration 	 bound=1238185.735747, 	 objsol=1238185.735747 
+number of solutions 12, first iteration 	 bound=1238853.909670, 	 objsol=1238853.909670 
+number of solutions 11, first iteration 	 bound=1238994.144600, 	 objsol=1238994.144600 
+number of solutions 11, first iteration 	 bound=1243152.502070, 	 objsol=1243152.502070 
+number of solutions 11, first iteration 	 bound=1242133.831560, 	 objsol=1242133.831560 
+number of solutions 11, first iteration 	 bound=1245744.530869, 	 objsol=1245744.530869 
+number of solutions 11, first iteration 	 bound=1245205.494079, 	 objsol=1245205.494079 
+number of solutions 11, first iteration 	 bound=1248084.273841, 	 objsol=1248084.273841 
+number of solutions 11, first iteration 	 bound=1249241.471795, 	 objsol=1249241.471795 
+number of solutions 11, first iteration 	 bound=1250241.022613, 	 objsol=1250241.022613 
+number of solutions 10, first iteration 	 bound=1250118.511084, 	 objsol=1250118.511084 
+number of solutions 11, first iteration 	 bound=1253178.719159, 	 objsol=1253178.719159 
+number of solutions 12, first iteration 	 bound=1253930.665989, 	 objsol=1253930.665989 
+number of solutions 10, first iteration 	 bound=1253037.031825, 	 objsol=1253037.031825 
+number of solutions 12, first iteration 	 bound=1257940.562495, 	 objsol=1257940.562495 
+number of solutions 11, first iteration 	 bound=1257422.828523, 	 objsol=1257422.828523 
+number of solutions 11, first iteration 	 bound=1258452.530575, 	 objsol=1258452.530575 
+number of solutions 11, first iteration 	 bound=1259552.124552, 	 objsol=1259552.124552 
+number of solutions 11, first iteration 	 bound=1259972.856173, 	 objsol=1259972.856173 
+number of solutions 11, first iteration 	 bound=1262403.465110, 	 objsol=1262403.465110 
+number of solutions 11, first iteration 	 bound=1262900.868471, 	 objsol=1262900.868471 
+number of solutions 11, first iteration 	 bound=1263814.432308, 	 objsol=1263814.432308 
+number of solutions 11, first iteration 	 bound=1265019.857869, 	 objsol=1265019.857869 
+number of solutions 10, first iteration 	 bound=1266205.328629, 	 objsol=1266205.328629 
+number of solutions 12, first iteration 	 bound=1267538.210346, 	 objsol=1267538.210346 
+number of solutions 10, first iteration 	 bound=1268243.082402, 	 objsol=1268243.082402 
+number of solutions 12, first iteration 	 bound=1268828.566504, 	 objsol=1268828.566504 
+number of solutions 11, first iteration 	 bound=1269633.765048, 	 objsol=1269633.765048 
+number of solutions 11, first iteration 	 bound=1271300.981826, 	 objsol=1271300.981826 
+number of solutions 11, first iteration 	 bound=1271570.232456, 	 objsol=1271570.232456 
+number of solutions 10, first iteration 	 bound=1272547.671274, 	 objsol=1272547.671274 
+number of solutions 12, first iteration 	 bound=1274184.663697, 	 objsol=1274184.663697 
+number of solutions 11, first iteration 	 bound=1275164.375242, 	 objsol=1275164.375242 
+number of solutions 10, first iteration 	 bound=1275586.924129, 	 objsol=1275586.924129 
+number of solutions 11, first iteration 	 bound=1276457.997280, 	 objsol=1276457.997280 
+number of solutions 12, first iteration 	 bound=1277820.536363, 	 objsol=1277820.536363 
+number of solutions 10, first iteration 	 bound=1278275.750896, 	 objsol=1278275.750896 
+number of solutions 12, first iteration 	 bound=1278990.490910, 	 objsol=1278990.490910 
+number of solutions 10, first iteration 	 bound=1280564.976832, 	 objsol=1280564.976832 
+number of solutions 11, first iteration 	 bound=1281073.034072, 	 objsol=1281073.034072 
+number of solutions 11, first iteration 	 bound=1281705.488007, 	 objsol=1281705.488007 
+number of solutions 11, first iteration 	 bound=1282343.981097, 	 objsol=1282343.981097 
+number of solutions 12, first iteration 	 bound=1283767.327581, 	 objsol=1283767.327581 
+number of solutions 11, first iteration 	 bound=1284804.105702, 	 objsol=1284804.105702 
+number of solutions 11, first iteration 	 bound=1284160.362654, 	 objsol=1284160.362654 
+number of solutions 11, first iteration 	 bound=1285710.159096, 	 objsol=1285710.159096 
+number of solutions 11, first iteration 	 bound=1285496.091083, 	 objsol=1285496.091083 
+number of solutions 11, first iteration 	 bound=1288033.554846, 	 objsol=1288033.554846 
+number of solutions 11, first iteration 	 bound=1286971.082761, 	 objsol=1286971.082761 
+number of solutions 11, first iteration 	 bound=1288215.915504, 	 objsol=1288215.915504 
+number of solutions 11, first iteration 	 bound=1289848.363697, 	 objsol=1289848.363697 
+number of solutions 11, first iteration 	 bound=1289030.663945, 	 objsol=1289030.663945 
+number of solutions 11, first iteration 	 bound=1290973.188680, 	 objsol=1290973.188680 
+number of solutions 11, first iteration 	 bound=1290492.003343, 	 objsol=1290492.003343 
+number of solutions 11, first iteration 	 bound=1291848.086971, 	 objsol=1291848.086971 
+number of solutions 11, first iteration 	 bound=1292324.408432, 	 objsol=1292324.408432 
+number of solutions 11, first iteration 	 bound=1292487.812085, 	 objsol=1292487.812085 
+number of solutions 11, first iteration 	 bound=1293609.540066, 	 objsol=1293609.540066 
+number of solutions 11, first iteration 	 bound=1294633.223598, 	 objsol=1294633.223598 
+number of solutions 11, first iteration 	 bound=1294686.072122, 	 objsol=1294686.072122 
+number of solutions 10, first iteration 	 bound=1296050.502384, 	 objsol=1296050.502384 
+number of solutions 12, first iteration 	 bound=1296017.253994, 	 objsol=1296017.253994 
+number of solutions 11, first iteration 	 bound=1296937.429607, 	 objsol=1296937.429607 
+number of solutions 11, first iteration 	 bound=1297950.589067, 	 objsol=1297950.589067 
+number of solutions 10, first iteration 	 bound=1297811.840367, 	 objsol=1297811.840367 
+number of solutions 12, first iteration 	 bound=1297651.375125, 	 objsol=1297651.375125 
+number of solutions 11, first iteration 	 bound=1299574.754722, 	 objsol=1299574.754722 
+number of solutions 11, first iteration 	 bound=1299551.658085, 	 objsol=1299551.658085 
+number of solutions 11, first iteration 	 bound=1300320.153238, 	 objsol=1300320.153238 
+number of solutions 11, first iteration 	 bound=1300499.048157, 	 objsol=1300499.048157 
+number of solutions 11, first iteration 	 bound=1301639.480237, 	 objsol=1301639.480237 
+number of solutions 10, first iteration 	 bound=1301180.641490, 	 objsol=1301180.641490 
+number of solutions 11, first iteration 	 bound=1302433.129828, 	 objsol=1302433.129828 
+number of solutions 12, first iteration 	 bound=1302600.605011, 	 objsol=1302600.605011 
+number of solutions 11, first iteration 	 bound=1303916.660353, 	 objsol=1303916.660353 
+number of solutions 11, first iteration 	 bound=1303446.451767, 	 objsol=1303446.451767 
+number of solutions 11, first iteration 	 bound=1304125.629501, 	 objsol=1304125.629501 
+number of solutions 11, first iteration 	 bound=1305213.350109, 	 objsol=1305213.350109 
+number of solutions 11, first iteration 	 bound=1304806.278512, 	 objsol=1304806.278512 
+number of solutions 11, first iteration 	 bound=1305683.240203, 	 objsol=1305683.240203 
+number of solutions 11, first iteration 	 bound=1306274.999014, 	 objsol=1306274.999014 
+number of solutions 11, first iteration 	 bound=1306391.252107, 	 objsol=1306391.252107 
+number of solutions 10, first iteration 	 bound=1307002.044072, 	 objsol=1307002.044072 
+number of solutions 12, first iteration 	 bound=1307220.398557, 	 objsol=1307220.398557 
+number of solutions 11, first iteration 	 bound=1307959.594172, 	 objsol=1307959.594172 
+number of solutions 11, first iteration 	 bound=1308102.980862, 	 objsol=1308102.980862 
+number of solutions 10, first iteration 	 bound=1309381.262877, 	 objsol=1309381.262877 
+number of solutions 12, first iteration 	 bound=1308136.300573, 	 objsol=1308136.300573 
+number of solutions 11, first iteration 	 bound=1310254.588455, 	 objsol=1310254.588455 
+number of solutions 11, first iteration 	 bound=1309472.664213, 	 objsol=1309472.664213 
+number of solutions 11, first iteration 	 bound=1310411.849449, 	 objsol=1310411.849449 
+number of solutions 11, first iteration 	 bound=1310747.333112, 	 objsol=1310747.333112 
+number of solutions 11, first iteration 	 bound=1310694.406371, 	 objsol=1310694.406371 
+number of solutions 10, first iteration 	 bound=1311742.132396, 	 objsol=1311742.132396 
+number of solutions 12, first iteration 	 bound=1311729.769803, 	 objsol=1311729.769803 
+number of solutions 11, first iteration 	 bound=1312201.479464, 	 objsol=1312201.479464 
+number of solutions 10, first iteration 	 bound=1313017.181496, 	 objsol=1313017.181496 
+number of solutions 12, first iteration 	 bound=1312766.479616, 	 objsol=1312766.479616 
+number of solutions 10, first iteration 	 bound=1313320.013192, 	 objsol=1313320.013192 
+number of solutions 11, first iteration 	 bound=1313754.621536, 	 objsol=1313754.621536 
+number of solutions 12, first iteration 	 bound=1313839.823459, 	 objsol=1313839.823459 
+number of solutions 11, first iteration 	 bound=1314132.963333, 	 objsol=1314132.963333 
+number of solutions 11, first iteration 	 bound=1314707.356195, 	 objsol=1314707.356195 
+number of solutions 11, first iteration 	 bound=1314971.923391, 	 objsol=1314971.923391 
+number of solutions 11, first iteration 	 bound=1315150.783123, 	 objsol=1315150.783123 
+number of solutions 10, first iteration 	 bound=1315833.627176, 	 objsol=1315833.627176 
+number of solutions 12, first iteration 	 bound=1315961.754241, 	 objsol=1315961.754241 
+number of solutions 10, first iteration 	 bound=1316377.703985, 	 objsol=1316377.703985 
+number of solutions 12, first iteration 	 bound=1316347.105593, 	 objsol=1316347.105593 
+number of solutions 11, first iteration 	 bound=1316843.151918, 	 objsol=1316843.151918 
+number of solutions 10, first iteration 	 bound=1317357.843619, 	 objsol=1317357.843619 
+number of solutions 12, first iteration 	 bound=1317507.001805, 	 objsol=1317507.001805 
+number of solutions 11, first iteration 	 bound=1317896.556602, 	 objsol=1317896.556602 
+number of solutions 11, first iteration 	 bound=1317820.862070, 	 objsol=1317820.862070 
+number of solutions 11, first iteration 	 bound=1318516.372697, 	 objsol=1318516.372697 
+number of solutions 11, first iteration 	 bound=1318621.136050, 	 objsol=1318621.136050 
+number of solutions 10, first iteration 	 bound=1318859.567895, 	 objsol=1318859.567895 
+number of solutions 12, first iteration 	 bound=1319000.843419, 	 objsol=1319000.843419 
+number of solutions 11, first iteration 	 bound=1319567.569621, 	 objsol=1319567.569621 
+number of solutions 11, first iteration 	 bound=1319691.606792, 	 objsol=1319691.606792 
+number of solutions 11, first iteration 	 bound=1319940.385938, 	 objsol=1319940.385938 
+number of solutions 11, first iteration 	 bound=1320349.870713, 	 objsol=1320349.870713 
+number of solutions 11, first iteration 	 bound=1320387.194990, 	 objsol=1320387.194990 
+number of solutions 11, first iteration 	 bound=1320463.880387, 	 objsol=1320463.880387 
+number of solutions 11, first iteration 	 bound=1321216.290686, 	 objsol=1321216.290686 
+number of solutions 11, first iteration 	 bound=1320844.010067, 	 objsol=1320844.010067 
+number of solutions 11, first iteration 	 bound=1321816.561185, 	 objsol=1321816.561185 
+number of solutions 11, first iteration 	 bound=1321394.309372, 	 objsol=1321394.309372 
+number of solutions 11, first iteration 	 bound=1322042.897186, 	 objsol=1322042.897186 
+number of solutions 11, first iteration 	 bound=1322074.670469, 	 objsol=1322074.670469 
+number of solutions 11, first iteration 	 bound=1322470.617749, 	 objsol=1322470.617749 
+number of solutions 11, first iteration 	 bound=1322540.369606, 	 objsol=1322540.369606 
+number of solutions 10, first iteration 	 bound=1322624.341772, 	 objsol=1322624.341772 
+number of solutions 12, first iteration 	 bound=1323133.150634, 	 objsol=1323133.150634 
+number of solutions 11, first iteration 	 bound=1323192.112008, 	 objsol=1323192.112008 
+number of solutions 10, first iteration 	 bound=1323472.765681, 	 objsol=1323472.765681 
+number of solutions 12, first iteration 	 bound=1323644.893299, 	 objsol=1323644.893299 
+number of solutions 10, first iteration 	 bound=1323794.986011, 	 objsol=1323794.986011 
+number of solutions 12, first iteration 	 bound=1324076.938053, 	 objsol=1324076.938053 
+number of solutions 10, first iteration 	 bound=1324264.958891, 	 objsol=1324264.958891 
+number of solutions 12, first iteration 	 bound=1324442.566244, 	 objsol=1324442.566244 
+number of solutions 11, first iteration 	 bound=1324715.952118, 	 objsol=1324715.952118 
+number of solutions 11, first iteration 	 bound=1325062.688091, 	 objsol=1325062.688091 
+number of solutions 11, first iteration 	 bound=1325001.755249, 	 objsol=1325001.755249 
+number of solutions 11, first iteration 	 bound=1325287.710175, 	 objsol=1325287.710175 
+number of solutions 11, first iteration 	 bound=1325353.478519, 	 objsol=1325353.478519 
+number of solutions 11, first iteration 	 bound=1325680.383714, 	 objsol=1325680.383714 
+number of solutions 10, first iteration 	 bound=1325820.296257, 	 objsol=1325820.296257 
+number of solutions 12, first iteration 	 bound=1325875.913080, 	 objsol=1325875.913080 
+number of solutions 11, first iteration 	 bound=1326046.872352, 	 objsol=1326046.872352 
+number of solutions 11, first iteration 	 bound=1326126.935796, 	 objsol=1326126.935796 
+number of solutions 10, first iteration 	 bound=1326501.345304, 	 objsol=1326501.345304 
+number of solutions 12, first iteration 	 bound=1326564.562640, 	 objsol=1326564.562640 
+number of solutions 11, first iteration 	 bound=1326911.515231, 	 objsol=1326911.515231 
+number of solutions 11, first iteration 	 bound=1326768.239443, 	 objsol=1326768.239443 
+number of solutions 11, first iteration 	 bound=1327096.455160, 	 objsol=1327096.455160 
+number of solutions 11, first iteration 	 bound=1327107.467419, 	 objsol=1327107.467419 
+number of solutions 11, first iteration 	 bound=1327515.664580, 	 objsol=1327515.664580 
+number of solutions 11, first iteration 	 bound=1327380.722438, 	 objsol=1327380.722438 
+number of solutions 11, first iteration 	 bound=1327775.196893, 	 objsol=1327775.196893 
+number of solutions 11, first iteration 	 bound=1327787.208340, 	 objsol=1327787.208340 
+number of solutions 11, first iteration 	 bound=1328041.546465, 	 objsol=1328041.546465 
+number of solutions 11, first iteration 	 bound=1327851.807392, 	 objsol=1327851.807392 
+number of solutions 11, first iteration 	 bound=1328410.976074, 	 objsol=1328410.976074 
+number of solutions 11, first iteration 	 bound=1328197.384069, 	 objsol=1328197.384069 
+number of solutions 11, first iteration 	 bound=1328564.688747, 	 objsol=1328564.688747 
+number of solutions 11, first iteration 	 bound=1328678.671736, 	 objsol=1328678.671736 
+number of solutions 11, first iteration 	 bound=1328775.978795, 	 objsol=1328775.978795 
+number of solutions 11, first iteration 	 bound=1328887.841322, 	 objsol=1328887.841322 
+number of solutions 11, first iteration 	 bound=1329055.036630, 	 objsol=1329055.036630 
+number of solutions 10, first iteration 	 bound=1329044.223776, 	 objsol=1329044.223776 
+number of solutions 12, first iteration 	 bound=1329348.721319, 	 objsol=1329348.721319 
+number of solutions 11, first iteration 	 bound=1329225.030464, 	 objsol=1329225.030464 
+number of solutions 11, first iteration 	 bound=1329592.064811, 	 objsol=1329592.064811 
+number of solutions 11, first iteration 	 bound=1329533.261170, 	 objsol=1329533.261170 
+number of solutions 11, first iteration 	 bound=1329768.225012, 	 objsol=1329768.225012 
+number of solutions 11, first iteration 	 bound=1329834.781920, 	 objsol=1329834.781920 
+number of solutions 10, first iteration 	 bound=1329959.481059, 	 objsol=1329959.481059 
+number of solutions 12, first iteration 	 bound=1329998.049238, 	 objsol=1329998.049238 
+number of solutions 11, first iteration 	 bound=1330194.244360, 	 objsol=1330194.244360 
+number of solutions 11, first iteration 	 bound=1330258.236306, 	 objsol=1330258.236306 
+number of solutions 11, first iteration 	 bound=1330297.805555, 	 objsol=1330297.805555 
+number of solutions 11, first iteration 	 bound=1330487.920227, 	 objsol=1330487.920227 
+number of solutions 11, first iteration 	 bound=1330499.755440, 	 objsol=1330499.755440 
+number of solutions 11, first iteration 	 bound=1330650.857400, 	 objsol=1330650.857400 
+number of solutions 11, first iteration 	 bound=1330761.816419, 	 objsol=1330761.816419 
+number of solutions 11, first iteration 	 bound=1330876.275775, 	 objsol=1330876.275775 
+number of solutions 11, first iteration 	 bound=1331012.256430, 	 objsol=1331012.256430 
+number of solutions 11, first iteration 	 bound=1330986.690295, 	 objsol=1330986.690295 
+number of solutions 11, first iteration 	 bound=1331069.452240, 	 objsol=1331069.452240 
+number of solutions 11, first iteration 	 bound=1331219.087946, 	 objsol=1331219.087946 
+number of solutions 11, first iteration 	 bound=1331299.061943, 	 objsol=1331299.061943 
+number of solutions 11, first iteration 	 bound=1331394.712258, 	 objsol=1331394.712258 
+number of solutions 11, first iteration 	 bound=1331437.910522, 	 objsol=1331437.910522 
+number of solutions 11, first iteration 	 bound=1331569.509084, 	 objsol=1331569.509084 
+number of solutions 11, first iteration 	 bound=1331590.246112, 	 objsol=1331590.246112 
+number of solutions 10, first iteration 	 bound=1331707.442314, 	 objsol=1331707.442314 
+number of solutions 11, first iteration 	 bound=1331729.271918, 	 objsol=1331729.271918 
+number of solutions 11, first iteration 	 bound=1331925.588798, 	 objsol=1331925.588798 
+number of solutions 12, first iteration 	 bound=1331901.449987, 	 objsol=1331901.449987 
+number of solutions 10, first iteration 	 bound=1332020.857836, 	 objsol=1332020.857836 
+number of solutions 12, first iteration 	 bound=1332045.244544, 	 objsol=1332045.244544 
+number of solutions 11, first iteration 	 bound=1332216.620575, 	 objsol=1332216.620575 
+number of solutions 11, first iteration 	 bound=1332139.685550, 	 objsol=1332139.685550 
+number of solutions 11, first iteration 	 bound=1332404.915399, 	 objsol=1332404.915399 
+number of solutions 10, first iteration 	 bound=1332320.827795, 	 objsol=1332320.827795 
+number of solutions 12, first iteration 	 bound=1332457.490111, 	 objsol=1332457.490111 
+number of solutions 10, first iteration 	 bound=1332492.645303, 	 objsol=1332492.645303 
+number of solutions 11, first iteration 	 bound=1332570.142631, 	 objsol=1332570.142631 
+number of solutions 12, first iteration 	 bound=1332660.381723, 	 objsol=1332660.381723 
+number of solutions 10, first iteration 	 bound=1332668.388465, 	 objsol=1332668.388465 
+number of solutions 11, first iteration 	 bound=1332824.223489, 	 objsol=1332824.223489 
+number of solutions 12, first iteration 	 bound=1332794.470086, 	 objsol=1332794.470086 
+number of solutions 11, first iteration 	 bound=1332904.600899, 	 objsol=1332904.600899 
+number of solutions 11, first iteration 	 bound=1332958.272951, 	 objsol=1332958.272951 
+number of solutions 11, first iteration 	 bound=1332971.471139, 	 objsol=1332971.471139 
+number of solutions 11, first iteration 	 bound=1333096.368669, 	 objsol=1333096.368669 
+number of solutions 10, first iteration 	 bound=1333076.489937, 	 objsol=1333076.489937 
+number of solutions 12, first iteration 	 bound=1333173.559068, 	 objsol=1333173.559068 
+number of solutions 11, first iteration 	 bound=1333216.631336, 	 objsol=1333216.631336 
+number of solutions 11, first iteration 	 bound=1333278.178399, 	 objsol=1333278.178399 
+number of solutions 11, first iteration 	 bound=1333354.357143, 	 objsol=1333354.357143 
+number of solutions 11, first iteration 	 bound=1333373.487941, 	 objsol=1333373.487941 
+number of solutions 11, first iteration 	 bound=1333417.063195, 	 objsol=1333417.063195 
+number of solutions 11, first iteration 	 bound=1333474.826859, 	 objsol=1333474.826859 
+number of solutions 11, first iteration 	 bound=1333531.509974, 	 objsol=1333531.509974 
+number of solutions 11, first iteration 	 bound=1333574.186483, 	 objsol=1333574.186483 
+number of solutions 11, first iteration 	 bound=1333626.861353, 	 objsol=1333626.861353 
+number of solutions 10, first iteration 	 bound=1333654.696217, 	 objsol=1333654.696217 
+number of solutions 12, first iteration 	 bound=1333721.697539, 	 objsol=1333721.697539 
+number of solutions 11, first iteration 	 bound=1333758.983065, 	 objsol=1333758.983065 
+number of solutions 11, first iteration 	 bound=1333810.811883, 	 objsol=1333810.811883 
+number of solutions 11, first iteration 	 bound=1333849.718520, 	 objsol=1333849.718520 
+number of solutions 11, first iteration 	 bound=1333894.968378, 	 objsol=1333894.968378 
+number of solutions 11, first iteration 	 bound=1333920.772542, 	 objsol=1333920.772542 
+number of solutions 11, first iteration 	 bound=1333966.525451, 	 objsol=1333966.525451 
+number of solutions 11, first iteration 	 bound=1333983.140540, 	 objsol=1333983.140540 
+number of solutions 11, first iteration 	 bound=1334074.116580, 	 objsol=1334074.116580 
+number of solutions 11, first iteration 	 bound=1334075.255517, 	 objsol=1334075.255517 
+number of solutions 11, first iteration 	 bound=1334114.825615, 	 objsol=1334114.825615 
+number of solutions 11, first iteration 	 bound=1334170.619656, 	 objsol=1334170.619656 
+number of solutions 11, first iteration 	 bound=1334195.612049, 	 objsol=1334195.612049 
+number of solutions 10, first iteration 	 bound=1334245.668750, 	 objsol=1334245.668750 
+number of solutions 11, first iteration 	 bound=1334258.050190, 	 objsol=1334258.050190 
+number of solutions 12, first iteration 	 bound=1334312.563191, 	 objsol=1334312.563191 
+number of solutions 10, first iteration 	 bound=1334327.017952, 	 objsol=1334327.017952 
+number of solutions 12, first iteration 	 bound=1334376.638630, 	 objsol=1334376.638630 
+number of solutions 11, first iteration 	 bound=1334414.628163, 	 objsol=1334414.628163 
+number of solutions 11, first iteration 	 bound=1334428.952985, 	 objsol=1334428.952985 
+number of solutions 11, first iteration 	 bound=1334473.878946, 	 objsol=1334473.878946 
+number of solutions 11, first iteration 	 bound=1334507.176953, 	 objsol=1334507.176953 
+number of solutions 11, first iteration 	 bound=1334513.504906, 	 objsol=1334513.504906 
+number of solutions 11, first iteration 	 bound=1334560.653526, 	 objsol=1334560.653526 
+number of solutions 10, first iteration 	 bound=1334586.827564, 	 objsol=1334586.827564 
+number of solutions 12, first iteration 	 bound=1334618.555183, 	 objsol=1334618.555183 
+number of solutions 11, first iteration 	 bound=1334612.959942, 	 objsol=1334612.959942 
+number of solutions 11, first iteration 	 bound=1334693.196304, 	 objsol=1334693.196304 
+number of solutions 11, first iteration 	 bound=1334682.883282, 	 objsol=1334682.883282 
+number of solutions 11, first iteration 	 bound=1334735.514998, 	 objsol=1334735.514998 
+number of solutions 11, first iteration 	 bound=1334735.834499, 	 objsol=1334735.834499 
+number of solutions 10, first iteration 	 bound=1334776.229684, 	 objsol=1334776.229684 
+number of solutions 11, first iteration 	 bound=1334801.656589, 	 objsol=1334801.656589 
+number of solutions 11, first iteration 	 bound=1334819.280544, 	 objsol=1334819.280544 
+number of solutions 11, first iteration 	 bound=1334852.898440, 	 objsol=1334852.898440 
+number of solutions 12, first iteration 	 bound=1334861.696422, 	 objsol=1334861.696422 
+number of solutions 10, first iteration 	 bound=1334889.366810, 	 objsol=1334889.366810 
+number of solutions 12, first iteration 	 bound=1334926.491751, 	 objsol=1334926.491751 
+number of solutions 11, first iteration 	 bound=1334944.734357, 	 objsol=1334944.734357 
+number of solutions 10, first iteration 	 bound=1334952.108667, 	 objsol=1334952.108667 
+number of solutions 12, first iteration 	 bound=1334987.553022, 	 objsol=1334987.553022 
+number of solutions 11, first iteration 	 bound=1335005.212554, 	 objsol=1335005.212554 
+number of solutions 11, first iteration 	 bound=1335028.595335, 	 objsol=1335028.595335 
+number of solutions 10, first iteration 	 bound=1335027.743058, 	 objsol=1335027.743058 
+number of solutions 12, first iteration 	 bound=1335070.312102, 	 objsol=1335070.312102 
+number of solutions 11, first iteration 	 bound=1335089.019878, 	 objsol=1335089.019878 
+number of solutions 11, first iteration 	 bound=1335106.853458, 	 objsol=1335106.853458 
+number of solutions 10, first iteration 	 bound=1335124.059780, 	 objsol=1335124.059780 
+number of solutions 12, first iteration 	 bound=1335148.839438, 	 objsol=1335148.839438 
+number of solutions 11, first iteration 	 bound=1335147.964581, 	 objsol=1335147.964581 
+number of solutions 10, first iteration 	 bound=1335184.533971, 	 objsol=1335184.533971 
+number of solutions 12, first iteration 	 bound=1335194.164261, 	 objsol=1335194.164261 
+number of solutions 11, first iteration 	 bound=1335222.948855, 	 objsol=1335222.948855 
+number of solutions 11, first iteration 	 bound=1335225.977660, 	 objsol=1335225.977660 
+number of solutions 10, first iteration 	 bound=1335254.500253, 	 objsol=1335254.500253 
+number of solutions 11, first iteration 	 bound=1335247.820711, 	 objsol=1335247.820711 
+number of solutions 12, first iteration 	 bound=1335280.710728, 	 objsol=1335280.710728 
+number of solutions 11, first iteration 	 bound=1335303.400319, 	 objsol=1335303.400319 
+number of solutions 11, first iteration 	 bound=1335311.813631, 	 objsol=1335311.813631 
+number of solutions 11, first iteration 	 bound=1335333.072998, 	 objsol=1335333.072998 
+number of solutions 10, first iteration 	 bound=1335342.195150, 	 objsol=1335342.195150 
+number of solutions 12, first iteration 	 bound=1335368.015817, 	 objsol=1335368.015817 
+number of solutions 11, first iteration 	 bound=1335366.553146, 	 objsol=1335366.553146 
+number of solutions 11, first iteration 	 bound=1335396.219658, 	 objsol=1335396.219658 
+number of solutions 11, first iteration 	 bound=1335386.985085, 	 objsol=1335386.985085 
+number of solutions 11, first iteration 	 bound=1335415.334271, 	 objsol=1335415.334271 
+number of solutions 11, first iteration 	 bound=1335427.333966, 	 objsol=1335427.333966 
+number of solutions 10, first iteration 	 bound=1335432.422527, 	 objsol=1335432.422527 
+number of solutions 11, first iteration 	 bound=1335461.464101, 	 objsol=1335461.464101 
+number of solutions 11, first iteration 	 bound=1335452.951670, 	 objsol=1335452.951670 
+number of solutions 12, first iteration 	 bound=1335476.476959, 	 objsol=1335476.476959 
+number of solutions 11, first iteration 	 bound=1335489.220585, 	 objsol=1335489.220585 
+number of solutions 11, first iteration 	 bound=1335501.122677, 	 objsol=1335501.122677 
+number of solutions 11, first iteration 	 bound=1335501.249034, 	 objsol=1335501.249034 
+number of solutions 11, first iteration 	 bound=1335519.238392, 	 objsol=1335519.238392 
+number of solutions 11, first iteration 	 bound=1335528.943228, 	 objsol=1335528.943228 
+number of solutions 10, first iteration 	 bound=1335549.405702, 	 objsol=1335549.405702 
+number of solutions 12, first iteration 	 bound=1335553.446684, 	 objsol=1335553.446684 
+number of solutions 10, first iteration 	 bound=1335567.073889, 	 objsol=1335567.073889 
+number of solutions 11, first iteration 	 bound=1335578.131084, 	 objsol=1335578.131084 
+number of solutions 12, first iteration 	 bound=1335579.934689, 	 objsol=1335579.934689 
+number of solutions 10, first iteration 	 bound=1335589.291297, 	 objsol=1335589.291297 
+number of solutions 12, first iteration 	 bound=1335611.335049, 	 objsol=1335611.335049 
+number of solutions 11, first iteration 	 bound=1335602.160916, 	 objsol=1335602.160916 
+number of solutions 11, first iteration 	 bound=1335628.207138, 	 objsol=1335628.207138 
+number of solutions 11, first iteration 	 bound=1335613.353056, 	 objsol=1335613.353056 
+number of solutions 10, first iteration 	 bound=1335650.123719, 	 objsol=1335650.123719 
+number of solutions 12, first iteration 	 bound=1335637.064103, 	 objsol=1335637.064103 
+number of solutions 11, first iteration 	 bound=1335659.458265, 	 objsol=1335659.458265 
+number of solutions 11, first iteration 	 bound=1335661.553968, 	 objsol=1335661.553968 
+number of solutions 11, first iteration 	 bound=1335672.845075, 	 objsol=1335672.845075 
+number of solutions 10, first iteration 	 bound=1335685.068047, 	 objsol=1335685.068047 
+number of solutions 12, first iteration 	 bound=1335685.039485, 	 objsol=1335685.039485 
+number of solutions 10, first iteration 	 bound=1335694.732548, 	 objsol=1335694.732548 
+number of solutions 12, first iteration 	 bound=1335705.752318, 	 objsol=1335705.752318 
+number of solutions 11, first iteration 	 bound=1335703.643450, 	 objsol=1335703.643450 
+number of solutions 11, first iteration 	 bound=1335723.665903, 	 objsol=1335723.665903 
+number of solutions 11, first iteration 	 bound=1335720.340647, 	 objsol=1335720.340647 
+number of solutions 11, first iteration 	 bound=1335733.996851, 	 objsol=1335733.996851 
+number of solutions 11, first iteration 	 bound=1335737.751938, 	 objsol=1335737.751938 
+number of solutions 10, first iteration 	 bound=1335744.087831, 	 objsol=1335744.087831 
+number of solutions 12, first iteration 	 bound=1335756.295073, 	 objsol=1335756.295073 
+number of solutions 11, first iteration 	 bound=1335757.013146, 	 objsol=1335757.013146 
+number of solutions 11, first iteration 	 bound=1335764.120000, 	 objsol=1335764.120000 
+number of solutions 11, first iteration 	 bound=1335768.897066, 	 objsol=1335768.897066 
+number of solutions 11, first iteration 	 bound=1335773.021132, 	 objsol=1335773.021132 
+number of solutions 11, first iteration 	 bound=1335784.073263, 	 objsol=1335784.073263 
+number of solutions 10, first iteration 	 bound=1335784.387633, 	 objsol=1335784.387633 
+number of solutions 11, first iteration 	 bound=1335798.725227, 	 objsol=1335798.725227 
+number of solutions 11, first iteration 	 bound=1335795.990349, 	 objsol=1335795.990349 
+number of solutions 11, first iteration 	 bound=1335805.996268, 	 objsol=1335805.996268 
+number of solutions 12, first iteration 	 bound=1335810.757984, 	 objsol=1335810.757984 
+number of solutions 10, first iteration 	 bound=1335810.742379, 	 objsol=1335810.742379 
+number of solutions 11, first iteration 	 bound=1335822.569587, 	 objsol=1335822.569587 
+number of solutions 12, first iteration 	 bound=1335824.213961, 	 objsol=1335824.213961 
+number of solutions 11, first iteration 	 bound=1335828.015306, 	 objsol=1335828.015306 
+number of solutions 11, first iteration 	 bound=1335837.620035, 	 objsol=1335837.620035 
+number of solutions 10, first iteration 	 bound=1335839.199097, 	 objsol=1335839.199097 
+number of solutions 11, first iteration 	 bound=1335843.267974, 	 objsol=1335843.267974 
+number of solutions 12, first iteration 	 bound=1335849.292821, 	 objsol=1335849.292821 
+number of solutions 11, first iteration 	 bound=1335854.185243, 	 objsol=1335854.185243 
+number of solutions 10, first iteration 	 bound=1335858.372829, 	 objsol=1335858.372829 
+number of solutions 11, first iteration 	 bound=1335862.962237, 	 objsol=1335862.962237 
+number of solutions 11, first iteration 	 bound=1335863.627055, 	 objsol=1335863.627055 
+number of solutions 12, first iteration 	 bound=1335870.534754, 	 objsol=1335870.534754 
+number of solutions 10, first iteration 	 bound=1335875.173379, 	 objsol=1335875.173379 
+number of solutions 12, first iteration 	 bound=1335875.885217, 	 objsol=1335875.885217 
+number of solutions 10, first iteration 	 bound=1335882.977142, 	 objsol=1335882.977142 
+number of solutions 11, first iteration 	 bound=1335887.870095, 	 objsol=1335887.870095 
+number of solutions 12, first iteration 	 bound=1335888.117441, 	 objsol=1335888.117441 
+number of solutions 10, first iteration 	 bound=1335895.608220, 	 objsol=1335895.608220 
+number of solutions 12, first iteration 	 bound=1335896.700346, 	 objsol=1335896.700346 
+number of solutions 10, first iteration 	 bound=1335899.282689, 	 objsol=1335899.282689 
+number of solutions 12, first iteration 	 bound=1335904.905381, 	 objsol=1335904.905381 
+number of solutions 11, first iteration 	 bound=1335905.573075, 	 objsol=1335905.573075 
+number of solutions 11, first iteration 	 bound=1335912.308425, 	 objsol=1335912.308425 
+number of solutions 10, first iteration 	 bound=1335913.568791, 	 objsol=1335913.568791 
+number of solutions 12, first iteration 	 bound=1335916.690843, 	 objsol=1335916.690843 
+number of solutions 11, first iteration 	 bound=1335919.282865, 	 objsol=1335919.282865 
+number of solutions 11, first iteration 	 bound=1335924.741651, 	 objsol=1335924.741651 
+number of solutions 11, first iteration 	 bound=1335922.478460, 	 objsol=1335922.478460 
+number of solutions 11, first iteration 	 bound=1335928.170376, 	 objsol=1335928.170376 
+number of solutions 10, first iteration 	 bound=1335933.083436, 	 objsol=1335933.083436 
+number of solutions 12, first iteration 	 bound=1335932.067606, 	 objsol=1335932.067606 
+number of solutions 11, first iteration 	 bound=1335937.329801, 	 objsol=1335937.329801 
+number of solutions 10, first iteration 	 bound=1335939.158863, 	 objsol=1335939.158863 
+number of solutions 12, first iteration 	 bound=1335941.623180, 	 objsol=1335941.623180 
+number of solutions 11, first iteration 	 bound=1335944.628023, 	 objsol=1335944.628023 
+number of solutions 10, first iteration 	 bound=1335946.501050, 	 objsol=1335946.501050 
+number of solutions 12, first iteration 	 bound=1335949.689557, 	 objsol=1335949.689557 
+number of solutions 11, first iteration 	 bound=1335952.565278, 	 objsol=1335952.565278 
+number of solutions 11, first iteration 	 bound=1335954.240736, 	 objsol=1335954.240736 
+number of solutions 11, first iteration 	 bound=1335955.327706, 	 objsol=1335955.327706 
+number of solutions 11, first iteration 	 bound=1335960.483202, 	 objsol=1335960.483202 
+number of solutions 11, first iteration 	 bound=1335960.175556, 	 objsol=1335960.175556 
+number of solutions 11, first iteration 	 bound=1335963.302198, 	 objsol=1335963.302198 
+number of solutions 11, first iteration 	 bound=1335964.880074, 	 objsol=1335964.880074 
+number of solutions 11, first iteration 	 bound=1335968.516337, 	 objsol=1335968.516337 
+number of solutions 11, first iteration 	 bound=1335967.511665, 	 objsol=1335967.511665 
+number of solutions 10, first iteration 	 bound=1335972.533672, 	 objsol=1335972.533672 
+number of solutions 11, first iteration 	 bound=1335972.003720, 	 objsol=1335972.003720 
+number of solutions 12, first iteration 	 bound=1335975.454051, 	 objsol=1335975.454051 
+number of solutions 11, first iteration 	 bound=1335976.926362, 	 objsol=1335976.926362 
+number of solutions 10, first iteration 	 bound=1335980.278118, 	 objsol=1335980.278118 
+number of solutions 11, first iteration 	 bound=1335980.665449, 	 objsol=1335980.665449 
+number of solutions 12, first iteration 	 bound=1335982.741810, 	 objsol=1335982.741810 
+number of solutions 10, first iteration 	 bound=1335984.565528, 	 objsol=1335984.565528 
+number of solutions 12, first iteration 	 bound=1335985.721296, 	 objsol=1335985.721296 
+number of solutions 11, first iteration 	 bound=1335988.095886, 	 objsol=1335988.095886 
+number of solutions 11, first iteration 	 bound=1335989.969746, 	 objsol=1335989.969746 
+number of solutions 11, first iteration 	 bound=1335990.790103, 	 objsol=1335990.790103 
+number of solutions 11, first iteration 	 bound=1335992.781829, 	 objsol=1335992.781829 
+number of solutions 11, first iteration 	 bound=1335992.769406, 	 objsol=1335992.769406 
+number of solutions 11, first iteration 	 bound=1335994.815084, 	 objsol=1335994.815084 
+number of solutions 11, first iteration 	 bound=1335997.212355, 	 objsol=1335997.212355 
+number of solutions 11, first iteration 	 bound=1335998.436714, 	 objsol=1335998.436714 
+number of solutions 11, first iteration 	 bound=1335998.895707, 	 objsol=1335998.895707 
+number of solutions 11, first iteration 	 bound=1336001.198297, 	 objsol=1336001.198297 
+number of solutions 10, first iteration 	 bound=1336001.503636, 	 objsol=1336001.503636 
+number of solutions 11, first iteration 	 bound=1336004.371704, 	 objsol=1336004.371704 
+number of solutions 12, first iteration 	 bound=1336003.387836, 	 objsol=1336003.387836 
+number of solutions 10, first iteration 	 bound=1336006.386284, 	 objsol=1336006.386284 
+number of solutions 12, first iteration 	 bound=1336006.707511, 	 objsol=1336006.707511 
+number of solutions 10, first iteration 	 bound=1336009.205788, 	 objsol=1336009.205788 
+number of solutions 11, first iteration 	 bound=1336009.040206, 	 objsol=1336009.040206 
+number of solutions 12, first iteration 	 bound=1336011.026938, 	 objsol=1336011.026938 
+number of solutions 11, first iteration 	 bound=1336012.699560, 	 objsol=1336012.699560 
+number of solutions 11, first iteration 	 bound=1336013.328558, 	 objsol=1336013.328558 
+number of solutions 11, first iteration 	 bound=1336014.591169, 	 objsol=1336014.591169 
+number of solutions 11, first iteration 	 bound=1336015.235470, 	 objsol=1336015.235470 
+number of solutions 10, first iteration 	 bound=1336016.401289, 	 objsol=1336016.401289 
+number of solutions 12, first iteration 	 bound=1336017.683998, 	 objsol=1336017.683998 
+number of solutions 11, first iteration 	 bound=1336018.435939, 	 objsol=1336018.435939 
+number of solutions 11, first iteration 	 bound=1336019.260835, 	 objsol=1336019.260835 
+number of solutions 11, first iteration 	 bound=1336019.875893, 	 objsol=1336019.875893 
+number of solutions 11, first iteration 	 bound=1336021.446091, 	 objsol=1336021.446091 
+number of solutions 10, first iteration 	 bound=1336021.629689, 	 objsol=1336021.629689 
+number of solutions 12, first iteration 	 bound=1336023.404976, 	 objsol=1336023.404976 
+number of solutions 11, first iteration 	 bound=1336024.374479, 	 objsol=1336024.374479 
+number of solutions 11, first iteration 	 bound=1336024.183851, 	 objsol=1336024.183851 
+number of solutions 11, first iteration 	 bound=1336025.775248, 	 objsol=1336025.775248 
+number of solutions 11, first iteration 	 bound=1336026.601759, 	 objsol=1336026.601759 
+number of solutions 10, first iteration 	 bound=1336026.936348, 	 objsol=1336026.936348 
+number of solutions 12, first iteration 	 bound=1336028.399138, 	 objsol=1336028.399138 
+number of solutions 11, first iteration 	 bound=1336028.954093, 	 objsol=1336028.954093 
+number of solutions 10, first iteration 	 bound=1336029.287274, 	 objsol=1336029.287274 
+number of solutions 12, first iteration 	 bound=1336030.377447, 	 objsol=1336030.377447 
+number of solutions 11, first iteration 	 bound=1336030.908848, 	 objsol=1336030.908848 
+number of solutions 11, first iteration 	 bound=1336031.620570, 	 objsol=1336031.620570 
+number of solutions 11, first iteration 	 bound=1336032.497969, 	 objsol=1336032.497969 
+number of solutions 11, first iteration 	 bound=1336033.283151, 	 objsol=1336033.283151 
+number of solutions 10, first iteration 	 bound=1336033.726584, 	 objsol=1336033.726584 
+number of solutions 12, first iteration 	 bound=1336034.316853, 	 objsol=1336034.316853 
+number of solutions 10, first iteration 	 bound=1336035.101205, 	 objsol=1336035.101205 
+number of solutions 12, first iteration 	 bound=1336035.391090, 	 objsol=1336035.391090 
+number of solutions 10, first iteration 	 bound=1336036.374105, 	 objsol=1336036.374105 
+number of solutions 11, first iteration 	 bound=1336036.425212, 	 objsol=1336036.425212 
+number of solutions 12, first iteration 	 bound=1336037.340358, 	 objsol=1336037.340358 
+number of solutions 11, first iteration 	 bound=1336037.975877, 	 objsol=1336037.975877 
+number of solutions 11, first iteration 	 bound=1336038.381303, 	 objsol=1336038.381303 
+number of solutions 11, first iteration 	 bound=1336039.216513, 	 objsol=1336039.216513 
+number of solutions 11, first iteration 	 bound=1336039.627969, 	 objsol=1336039.627969 
+number of solutions 11, first iteration 	 bound=1336040.233754, 	 objsol=1336040.233754 
+number of solutions 10, first iteration 	 bound=1336040.877488, 	 objsol=1336040.877488 
+number of solutions 11, first iteration 	 bound=1336041.044714, 	 objsol=1336041.044714 
+number of solutions 12, first iteration 	 bound=1336041.494222, 	 objsol=1336041.494222 
+number of solutions 10, first iteration 	 bound=1336042.208125, 	 objsol=1336042.208125 
+number of solutions 12, first iteration 	 bound=1336042.656467, 	 objsol=1336042.656467 
+number of solutions 10, first iteration 	 bound=1336042.886236, 	 objsol=1336042.886236 
+number of solutions 11, first iteration 	 bound=1336043.267605, 	 objsol=1336043.267605 
+number of solutions 12, first iteration 	 bound=1336044.056132, 	 objsol=1336044.056132 
+number of solutions 10, first iteration 	 bound=1336044.309351, 	 objsol=1336044.309351 
+number of solutions 12, first iteration 	 bound=1336044.791876, 	 objsol=1336044.791876 
+number of solutions 10, first iteration 	 bound=1336045.038204, 	 objsol=1336045.038204 
+number of solutions 12, first iteration 	 bound=1336045.710375, 	 objsol=1336045.710375 
+number of solutions 11, first iteration 	 bound=1336046.152488, 	 objsol=1336046.152488 
+number of solutions 11, first iteration 	 bound=1336046.334512, 	 objsol=1336046.334512 
+number of solutions 10, first iteration 	 bound=1336046.690252, 	 objsol=1336046.690252 
+number of solutions 12, first iteration 	 bound=1336047.178339, 	 objsol=1336047.178339 
+number of solutions 10, first iteration 	 bound=1336047.369217, 	 objsol=1336047.369217 
+number of solutions 12, first iteration 	 bound=1336048.120864, 	 objsol=1336048.120864 
+number of solutions 11, first iteration 	 bound=1336048.186851, 	 objsol=1336048.186851 
+number of solutions 11, first iteration 	 bound=1336048.353989, 	 objsol=1336048.353989 
+number of solutions 11, first iteration 	 bound=1336049.011752, 	 objsol=1336049.011752 
+number of solutions 10, first iteration 	 bound=1336049.261093, 	 objsol=1336049.261093 
+number of solutions 12, first iteration 	 bound=1336049.244917, 	 objsol=1336049.244917 
+number of solutions 11, first iteration 	 bound=1336049.713562, 	 objsol=1336049.713562 
+number of solutions 11, first iteration 	 bound=1336050.013839, 	 objsol=1336050.013839 
+number of solutions 10, first iteration 	 bound=1336050.558665, 	 objsol=1336050.558665 
+number of solutions 11, first iteration 	 bound=1336050.646385, 	 objsol=1336050.646385 
+number of solutions 12, first iteration 	 bound=1336051.087939, 	 objsol=1336051.087939 
+number of solutions 10, first iteration 	 bound=1336051.364397, 	 objsol=1336051.364397 
+number of solutions 12, first iteration 	 bound=1336051.701982, 	 objsol=1336051.701982 
+number of solutions 10, first iteration 	 bound=1336051.845631, 	 objsol=1336051.845631 
+number of solutions 12, first iteration 	 bound=1336052.002055, 	 objsol=1336052.002055 
+number of solutions 10, first iteration 	 bound=1336052.350198, 	 objsol=1336052.350198 
+number of solutions 12, first iteration 	 bound=1336052.589583, 	 objsol=1336052.589583 
+number of solutions 10, first iteration 	 bound=1336052.701890, 	 objsol=1336052.701890 
+number of solutions 12, first iteration 	 bound=1336053.086672, 	 objsol=1336053.086672 
+number of solutions 11, first iteration 	 bound=1336053.109531, 	 objsol=1336053.109531 
+number of solutions 11, first iteration 	 bound=1336053.608213, 	 objsol=1336053.608213 
+number of solutions 11, first iteration 	 bound=1336053.700714, 	 objsol=1336053.700714 
+number of solutions 11, first iteration 	 bound=1336054.169026, 	 objsol=1336054.169026 
+number of solutions 11, first iteration 	 bound=1336054.169603, 	 objsol=1336054.169603 
+number of solutions 10, first iteration 	 bound=1336054.390320, 	 objsol=1336054.390320 
+number of solutions 12, first iteration 	 bound=1336054.620583, 	 objsol=1336054.620583 
+number of solutions 11, first iteration 	 bound=1336054.756619, 	 objsol=1336054.756619 
+number of solutions 11, first iteration 	 bound=1336054.836978, 	 objsol=1336054.836978 
+number of solutions 11, first iteration 	 bound=1336055.284188, 	 objsol=1336055.284188 
+number of solutions 11, first iteration 	 bound=1336055.287969, 	 objsol=1336055.287969 
+number of solutions 11, first iteration 	 bound=1336055.486929, 	 objsol=1336055.486929 
+number of solutions 11, first iteration 	 bound=1336055.905396, 	 objsol=1336055.905396 
+number of solutions 11, first iteration 	 bound=1336055.882554, 	 objsol=1336055.882554 
+number of solutions 11, first iteration 	 bound=1336056.120410, 	 objsol=1336056.120410 
+number of solutions 11, first iteration 	 bound=1336056.237586, 	 objsol=1336056.237586 
+number of solutions 11, first iteration 	 bound=1336056.418744, 	 objsol=1336056.418744 
+number of solutions 11, first iteration 	 bound=1336056.584080, 	 objsol=1336056.584080 
+number of solutions 11, first iteration 	 bound=1336056.728589, 	 objsol=1336056.728589 
+number of solutions 11, first iteration 	 bound=1336056.878163, 	 objsol=1336056.878163 
+number of solutions 11, first iteration 	 bound=1336057.088426, 	 objsol=1336057.088426 
+number of solutions 11, first iteration 	 bound=1336057.145057, 	 objsol=1336057.145057 
+number of solutions 11, first iteration 	 bound=1336057.478520, 	 objsol=1336057.478520 
+number of solutions 11, first iteration 	 bound=1336057.347685, 	 objsol=1336057.347685 
+number of solutions 10, first iteration 	 bound=1336057.681190, 	 objsol=1336057.681190 
+number of solutions 12, first iteration 	 bound=1336057.685034, 	 objsol=1336057.685034 
+number of solutions 11, first iteration 	 bound=1336058.020093, 	 objsol=1336058.020093 
+number of solutions 10, first iteration 	 bound=1336057.931081, 	 objsol=1336057.931081 
+number of solutions 12, first iteration 	 bound=1336058.155088, 	 objsol=1336058.155088 
+number of solutions 10, first iteration 	 bound=1336058.338127, 	 objsol=1336058.338127 
+number of solutions 12, first iteration 	 bound=1336058.378536, 	 objsol=1336058.378536 
+number of solutions 11, first iteration 	 bound=1336058.505483, 	 objsol=1336058.505483 
+number of solutions 11, first iteration 	 bound=1336058.656019, 	 objsol=1336058.656019 
+number of solutions 11, first iteration 	 bound=1336058.750519, 	 objsol=1336058.750519 
+number of solutions 11, first iteration 	 bound=1336058.922288, 	 objsol=1336058.922288 
+number of solutions 11, first iteration 	 bound=1336058.897930, 	 objsol=1336058.897930 
+number of solutions 11, first iteration 	 bound=1336059.107172, 	 objsol=1336059.107172 
+number of solutions 11, first iteration 	 bound=1336059.194546, 	 objsol=1336059.194546 
+number of solutions 11, first iteration 	 bound=1336059.324416, 	 objsol=1336059.324416 
+number of solutions 11, first iteration 	 bound=1336059.396593, 	 objsol=1336059.396593 
+number of solutions 11, first iteration 	 bound=1336059.554967, 	 objsol=1336059.554967 
+number of solutions 11, first iteration 	 bound=1336059.526521, 	 objsol=1336059.526521 
+number of solutions 11, first iteration 	 bound=1336059.707151, 	 objsol=1336059.707151 
+number of solutions 11, first iteration 	 bound=1336059.776496, 	 objsol=1336059.776496 
+number of solutions 11, first iteration 	 bound=1336059.848736, 	 objsol=1336059.848736 
+number of solutions 11, first iteration 	 bound=1336059.900018, 	 objsol=1336059.900018 
+number of solutions 11, first iteration 	 bound=1336060.091742, 	 objsol=1336060.091742 
+number of solutions 11, first iteration 	 bound=1336060.090568, 	 objsol=1336060.090568 
+number of solutions 11, first iteration 	 bound=1336060.246505, 	 objsol=1336060.246505 
+number of solutions 11, first iteration 	 bound=1336060.297851, 	 objsol=1336060.297851 
+number of solutions 11, first iteration 	 bound=1336060.365240, 	 objsol=1336060.365240 
+number of solutions 11, first iteration 	 bound=1336060.464773, 	 objsol=1336060.464773 
+number of solutions 11, first iteration 	 bound=1336060.600882, 	 objsol=1336060.600882 
+number of solutions 11, first iteration 	 bound=1336060.612843, 	 objsol=1336060.612843 
+number of solutions 11, first iteration 	 bound=1336060.693565, 	 objsol=1336060.693565 
+number of solutions 11, first iteration 	 bound=1336060.786258, 	 objsol=1336060.786258 
+number of solutions 11, first iteration 	 bound=1336060.840988, 	 objsol=1336060.840988 
+number of solutions 11, first iteration 	 bound=1336060.922385, 	 objsol=1336060.922385 
+number of solutions 10, first iteration 	 bound=1336060.980425, 	 objsol=1336060.980425 
+number of solutions 11, first iteration 	 bound=1336061.008804, 	 objsol=1336061.008804 
+number of solutions 11, first iteration 	 bound=1336061.064360, 	 objsol=1336061.064360 
+number of solutions 12, first iteration 	 bound=1336061.159374, 	 objsol=1336061.159374 
+number of solutions 10, first iteration 	 bound=1336061.144582, 	 objsol=1336061.144582 
+number of solutions 12, first iteration 	 bound=1336061.290351, 	 objsol=1336061.290351 
+number of solutions 10, first iteration 	 bound=1336061.335581, 	 objsol=1336061.335581 
+number of solutions 12, first iteration 	 bound=1336061.387480, 	 objsol=1336061.387480 
+number of solutions 11, first iteration 	 bound=1336061.446610, 	 objsol=1336061.446610 
+number of solutions 10, first iteration 	 bound=1336061.475750, 	 objsol=1336061.475750 
+number of solutions 12, first iteration 	 bound=1336061.599322, 	 objsol=1336061.599322 
+number of solutions 10, first iteration 	 bound=1336061.590993, 	 objsol=1336061.590993 
+number of solutions 12, first iteration 	 bound=1336061.680328, 	 objsol=1336061.680328 
+number of solutions 11, first iteration 	 bound=1336061.717224, 	 objsol=1336061.717224 
+number of solutions 11, first iteration 	 bound=1336061.765173, 	 objsol=1336061.765173 
+number of solutions 11, first iteration 	 bound=1336061.795407, 	 objsol=1336061.795407 
+number of solutions 11, first iteration 	 bound=1336061.820433, 	 objsol=1336061.820433 
+number of solutions 11, first iteration 	 bound=1336061.917459, 	 objsol=1336061.917459 
+number of solutions 11, first iteration 	 bound=1336061.911289, 	 objsol=1336061.911289 
+number of solutions 11, first iteration 	 bound=1336062.013914, 	 objsol=1336062.013914 
+number of solutions 11, first iteration 	 bound=1336062.026389, 	 objsol=1336062.026389 
+number of solutions 11, first iteration 	 bound=1336062.057948, 	 objsol=1336062.057948 
+number of solutions 10, first iteration 	 bound=1336062.132268, 	 objsol=1336062.132268 
+number of solutions 12, first iteration 	 bound=1336062.125467, 	 objsol=1336062.125467 
+number of solutions 11, first iteration 	 bound=1336062.177551, 	 objsol=1336062.177551 
+number of solutions 11, first iteration 	 bound=1336062.241709, 	 objsol=1336062.241709 
+number of solutions 11, first iteration 	 bound=1336062.268484, 	 objsol=1336062.268484 
+number of solutions 11, first iteration 	 bound=1336062.290532, 	 objsol=1336062.290532 
+number of solutions 11, first iteration 	 bound=1336062.338909, 	 objsol=1336062.338909 
+number of solutions 11, first iteration 	 bound=1336062.367768, 	 objsol=1336062.367768 
+number of solutions 11, first iteration 	 bound=1336062.390806, 	 objsol=1336062.390806 
+number of solutions 11, first iteration 	 bound=1336062.455449, 	 objsol=1336062.455449 
+number of solutions 9, first iteration 	 bound=1336062.871911, 	 objsol=1336062.871911 
+number of solutions 11, first iteration 	 bound=1336062.125530, 	 objsol=1336062.125530 
+number of solutions 9, first iteration 	 bound=1336062.948754, 	 objsol=1336062.948754 
diff --git a/src/re.cpp b/src/re.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..68f07c4c9ce78a5ffce3e1b6284bbec3f48815ca
--- /dev/null
+++ b/src/re.cpp
@@ -0,0 +1,1012 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  This file is part of the program and library             */
+/*         SCIP --- Solving Constraint Integer Programs                      */
+/*                                                                           */
+/*    Copyright (C) 2002-2020 Konrad-Zuse-Zentrum                            */
+/*                            fuer Informationstechnik Berlin                */
+/*                                                                           */
+/*  SCIP is distributed under the terms of the ZIB Academic License.         */
+/*                                                                           */
+/*  You should have received a copy of the ZIB Academic License              */
+/*  along with SCIP; see the file COPYING. If not visit scipopt.org.         */
+/*                                                                           */
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+/**@file   relax_lagr.c
+ * @ingroup OTHER_CFILES
+ * @brief  lagr relaxator
+ * @author Dawit Hailu  
+ */
+
+/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+//I'm gonna write this, just to check if it will upload right or not :) 
+//what's up bro, this is just to check if i can pull it on git. 
+//it worked buddy. now time to push it
+#include <assert.h>
+#include <string.h>
+#include <chrono>
+#include <iostream>
+#include <math.h>
+
+
+#include "relax_lagr.h"
+#include "scip/scipdefplugins.h"
+#include "scip/scip.h"
+#include "scip/cons_countsols.c"
+
+#include "probdata_lagr.h"
+#include "vardata_lagr.h"
+
+
+
+
+#define RELAX_NAME             "lagr"
+#define RELAX_DESC             "relaxator template"
+#define RELAX_PRIORITY         0
+#define RELAX_FREQ             0
+
+
+
+
+/*
+ * Data structures
+ */
+
+/* TODO: fill in the necessary relaxator data */
+
+/** relaxator data */
+struct SCIP_RelaxData
+
+{
+   SCIP_SOL* sol;         /**current solution(working solution)*/
+   SCIP_VARDATA* vardata;
+   SCIP_CONSDATA* consdata;
+   SCIP_Real* bestsolvals;
+   SCIP_Real* feasiblesol;
+   SCIP_Real* upperbound;
+};
+
+struct SCIP_VarData
+{
+   SCIP_VAR*                        var;
+   SCIP_CONS**                      VarConss;
+   int                              nVarConss;
+   SCIP_CONS**                      VarSlotConss;                   /**<contains all slot constraints containing the variable */
+   int                              NVarInBadConss;                 /**<number of slot constraints the variable is occuring in*/  
+   SCIP_Real                        varquotient;
+   int*                              consids;
+   int*                              varids;
+};
+
+
+/** destructor of relaxator to free user data (called when SCIP is exiting) */
+static
+SCIP_DECL_RELAXFREE(relaxFreelagr)
+{  /*lint --e{715}*/
+   SCIPerrorMessage("start executing lagr\n");
+   SCIP_RELAXDATA* relaxdata;
+   relaxdata = SCIPrelaxGetData(relax);
+   SCIPfreeBlockMemory(scip, &relaxdata);
+   SCIPrelaxSetData(relax,NULL);
+   
+   return SCIP_OKAY;
+}
+
+/** initialization method of relaxator (called after problem was transformed) */
+
+int SCIPvardataGetNVarInBadConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+ {
+    return vardata->NVarInBadConss;
+ }
+
+int* SCIPvardataGetvarids(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+ {
+    return vardata->varids;
+ }
+
+
+static
+SCIP_DECL_RELAXINIT(relaxInitlagr)
+{  /*lint --e{715}*/
+   
+   // SCIP* relaxscip;
+   // SCIP_HASHMAP* varmap;
+   // SCIP_HASHMAP* consmap;
+   // SCIP_CONS** conss;
+   // SCIP_PROBDATA* probdata;
+   // SCIP_VARDATA* vardata;
+
+   // SCIP_Real relaxval;
+   // SCIP_Bool valid;
+   // int nconss;
+   // int i;
+   // int counter;
+   // int id;
+
+
+   // // *lowerbound = -SCIPinfinity(scip);
+   // // *result = SCIP_DIDNOTRUN;
+
+   // /* we can only run if none of the present constraints expect their variables to be binary or integer during transformation */
+   // conss = SCIPgetConss(scip);
+   // nconss = SCIPgetNConss(scip);
+
+   // /* create the variable mapping hash map */
+   // SCIP_CALL( SCIPcreate(&relaxscip) );
+   // SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)) );
+   // valid = FALSE;
+   // SCIP_CALL( SCIPcopy(scip, relaxscip, varmap, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &valid) );
+   
+   // /**************************************************************************************************************/
+   // /*First,                                                                                                      */
+   // //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
+   // /***************************************************************************************************************/
+   // int nvars = SCIPgetNVars(relaxscip);
+   // SCIP_VAR** vars = SCIPgetVars(relaxscip);
+   // SCIP_VAR** varbuffers;
+   // int* badconss;
+   
+   // SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
+   // int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
+   // int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
+   // int* listnconsvars = SCIPlistnconsvars(probdata);
+   // int* listconsvarids = SCIPlistconsvarids(probdata);
+
+   // /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
+   // FILE* TimeCollector;
+   // TimeCollector = fopen("time.txt","w");
+   // SCIP_CLOCK* varslottime;                 //to help us record the time
+   // SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
+   // SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
+
+   // // int nconsvars=0;
+   // int* consids;
+
+   // SCIP_Real* weights;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
+
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
+
+   // for (int v = 0; v < nvars; v++)
+   // {
+   //    SCIP_VAR* var = vars[v];
+   //    weights[v]=SCIPvarGetObj(var);
+   // }
+
+   // for (int v = 0; v < nvars; v++)
+   // { 
+   //    int* varids;
+   //    int NVarInBadConss=0;
+   //    int nconsvars = 0;
+   //    SCIP_VAR* var = vars[v];
+
+   //    int varindex = SCIPvarGetIndex(var);                                    /* (2) */
+   //    assert(varindex!= NULL);
+
+   //    // printf("%s****%d\n",SCIPvarGetName(var),varindex);
+   //    for (int r = 0; r < nSlotConss; ++r)
+   //    {
+   //       id = badconss[r];
+   //       SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //       // printf("%s \t",SCIPconsGetName(cons));
+   //       SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
+   //       SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
+   //       if (!valid){
+   //       abort(); }
+
+   //       for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+   //       {
+   //          SCIP_VAR* varx = varbuffers[j];
+   //          int varbufindex = SCIPvarGetIndex(varx);
+   //          assert(varbufindex != NULL);
+   //          // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
+            
+            
+   //          /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+   //          if (varindex == varbufindex)                                           /* (9) */
+   //          {
+               
+   //             // VarSlotConss[NVarInBadConss] = cons;
+   //             consids[NVarInBadConss]=id;
+   //             NVarInBadConss++;
+   //             // printf(" %s \t,",SCIPconsGetName(cons));
+   //          }
+   //       }
+   //    }
+
+   //    SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
+   //    for(int t=0;t<NVarInBadConss;t++)
+   //    {
+   //       varids[t]=consids[t];
+   //       // printf("%d \t",varids[t]);
+   //    }
+
+   //    // vardata=SCIPvarGetData(var);
+   //    SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+   //    SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
+   //    vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
+   //    vardata->varids = varids;
+   //    // /**set the variable data to the variable*/
+   //    SCIPvarSetData(var,vardata);  
+   // }
+
+   // // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   
+
+   // FILE* AfterPreProcessing;
+   // AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
+
+   // // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+
+   // SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   // for(int r=0;r<nSlotConss;r++)
+   // {
+   //    id = badconss[r];
+   //    SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //    SCIP_CALL(SCIPdelCons(relaxscip,cons));
+   // }
+
+   // /******************************************************************************************************************/
+   // /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
+   // /* In the end, we will subtract this sum from the objective of the function.                                      */
+   // /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
+   // /******************************************************************************************************************/
+   // SCIP_Real* dualmultipliers;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
+   
+   // SCIP_Real* subgradients;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
+   // //initialize subgradients;
+   // SCIP_Real stepsize = 150.00000;
+   // SCIP_Real sumofduals=0;
+   // for ( int r = 0; r < nSlotConss; ++r)
+   // {
+   //    // id = badconss[r];
+   //    // SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //    //if k=1 iteration//
+   //    dualmultipliers[r] = 0;
+   //    sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+      
+   // }
+
+
+
+   // /*******************************************************************************************************/
+   // /* The reformulation of the problem can be written as follows                                          */
+   // //*>>>>>>>>>>>>>>>>>> min sum { (w[i]+sum{dual[j]})}x[i]-sum{dual[r]} <<<<<<<<<<<<                     */
+   // /*where i is nvars, j is NVarInBadConss, and r is nSlotConss for our case *******************************/
+   // /****************************************************************************************************************/
+   // /* The following function will add the following to the obj(weight) of the variable,                            */
+   // //*  the obj(weight) of var + the sum of the dualmultipliers of bad constraints which contains this variable    */
+   // /****************************************************************************************************************/
+  
+ 
+   // FILE* solutions;
+   // solutions = fopen("sol.txt","wr");
+   // FILE* dual;
+   // dual= fopen("dual.txt","wr");
+   // FILE* variableinfo; 
+   // variableinfo = fopen("var.txt","wr");
+   // FILE* subgrad;
+   // subgrad = fopen("subgrads.txt","wr");
+   // FILE* varobjects;
+   // varobjects=fopen("varobjs.txt","wr");
+   // FILE* lower;
+   // lower=fopen("lowerbounds.txt","wr");
+   
+
+   // int maxiter=125;
+   // fprintf(lower,"%d\n",maxiter);
+
+   // for(int iter=1;iter<=maxiter;iter++)
+   // {
+      
+   //    for(int v=0;v<nvars;v++)
+   //    {
+   //       SCIP_VAR* var = vars[v];
+   //       double sum =SCIPvarGetObj(var);
+         
+   //       vardata=SCIPvarGetData(var);
+   //       int* varids = SCIPvardataGetvarids(vardata); 
+   //       int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+
+   //       // printf("\n");
+   //       for(int t=0;t<NVarInBadConss;t++)
+   //       {
+   //          // printf("sum = %f, varid %d, dual %f, ", sum, varids[t],dualmultipliers[varids[t]]);
+   //          sum += dualmultipliers[varids[t]];
+   //          // fprintf(varobjects,"{%d, %f, %f\t",varids[t], dualmultipliers[varids[t]],sum);
+   //       }
+   //       // fprintf(varobjects,"}\n\n");
+   //       SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
+   //       // if(sum>weights[v]){printf("new weight %f",SCIPvarGetObj(var));}
+         
+   //    }
+   //    // printf("weight for v1 %f \t:= conss",solvals[1]);
+   //    // for(int s=0; s<listnconsvars[0];++s)
+   //    // {
+   //    //    int id = listconsvarids[s];
+      
+   //    //    printf("(%s, duals = %f) \t",SCIPconsGetName(SCIPgetConss(scip)[id]), dualmultipliers[id]);
+   //    // }
+      
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+      
+   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
+   //    // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+   //    SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
+   //    // fclose(AfterPreProcessing);
+
+   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
+   //    SCIP_CALL( SCIPsolve(relaxscip) );
+   //    relaxval = SCIPgetPrimalbound(relaxscip);
+   //    // printf("\ndualbound %f, primalbound %f \n",SCIPgetDualbound(relaxscip),SCIPgetPrimalbound(relaxscip));
+   //    SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
+   //    /*get the best solution*/   
+   //    SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
+   //    SCIP_SOL** sols = SCIPgetSols(relaxscip);
+   //    int nsols = SCIPgetNSols(relaxscip);
+
+   //    SCIP_Real* solvals;
+   //    SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+1)); 
+   
+
+   //    /*text output*/
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   //    fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
+   //    // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
+
+   //    /*store the solution in solvals so we can later export it to subgradient function*/
+   //    SCIP_Real lowerbound=0;
+   //    SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
+   //    SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
+
+   //    SCIP_Real compare=0;
+   //    for (int v = 0; v<nvars; ++v)
+   //    {
+   //       compare += solvals[v]*weights[v]; 
+   //    }
+
+   //    printf("compare value %f\n",compare);
+   //    // for(int s=0;s<nsols;s++)
+   //    // {
+   //    //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
+   //    //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
+   //    //    SCIP_Real compare=0;
+   //    //    for (int v = 0; v<nvars; ++v)
+   //    //    {
+   //    //       compare += solvals[v]*weights[v]; 
+   //    //    }
+         
+   //    //    printf("compare value %f\n",compare);
+   //    //    if(compare>lowerbound){lowerbound==compare;} 
+   //    // }
+   //    // fprintf(dual,"now comes the biggest one\n");
+
+   //    // for(int s=0;s<nsols;s++)
+   //    // {
+   //    //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
+   //    //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
+   //    //    SCIP_Real compare=0;
+   //    //    for (int v = 0; v<nvars; ++v)
+   //    //    {
+   //    //       compare += solvals[v]*weights[v]; 
+   //    //    }
+   //    //    if(compare==lowerbound){break;} 
+   //    // }
+      
+      
+
+   //    stepsize = 15000/double(iter+1); 
+   //    // fprintf(solutions, "\niteration %d\n",iter);
+   //    // fprintf(dual, "\niteration %d\n",iter);
+   //    // fprintf(variableinfo, "\niteration %d\n",iter);
+   //    // fprintf(varobjects, "\niteration %d\n",iter);
+
+   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
+   //    // SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    // SCIP_CALL( SCIPtransformProb(relaxscip) );
+
+   //    counter = 0;
+   //    int checker = 0;
+   //    for(int r=0; r<nSlotConss;++r)
+   //    {
+   //       id = badconss[r];
+   //       double ax=-1;
+   //       for(int s=counter;s<(counter+listnconsvars[r]);++s)
+   //       {
+   //          // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
+   //          ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
+   //          // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
+            
+   //       }
+         
+   //       counter += listnconsvars[r];
+   //       if(ax>0){checker++;}
+   //       subgradients[r]=ax;
+   //       // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
+         
+   //    }
+   //    if(checker==0){printf("#*#*#*result found\n"); break;}
+
+   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
+   
+      
+
+      
+   //    for (int v = 0; v<nvars; ++v)
+   //    {
+   //       SCIP_VAR* var = vars[v];
+         
+   //       SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v])); 
+   //       // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
+   //       lowerbound += solvals[v]*weights[v]; 
+   //    }
+   //    fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
+   //    fprintf(lower,"%f\n",lowerbound);
+
+   //    // stepsize = (SCIPgetPrimalbound(relaxscip)-lowerbound)/getnorm(subgradients,nSlotConss,stepsize);
+   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    fprintf(solutions, "lowerbound = %f \n ", lowerbound);
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
+      
+   //    //add back the sum of the duals we subtracted from the main obj function
+
+   //    int sum=0;
+   //    sumofduals = 0;
+
+   //    for(int r=0; r<nSlotConss;++r)
+   //    { 
+   //       dualmultipliers[r] += subgradients[r]*stepsize;
+   //       if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
+         
+   //       sum+=dualmultipliers[r];
+   //       // fprintf(dual," then %f step size %f \n",dualmultipliers[r], stepsize);
+   //    }
+   //    sumofduals=sum;
+   //    // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n new dual found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   //    // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
+   // }
+   // SCIPfreeTransform(relaxscip);
+   // fclose(variableinfo);
+   // fclose(dual);
+   // fclose(subgrad);
+   // fclose(varobjects);
+   // fclose(solutions);
+   // fclose(lower);
+
+
+   /* free memory */
+   // SCIPhashmapFree(&varmap);
+   // SCIP_CALL( SCIPfree(&relaxscip) );
+
+   
+
+   return SCIP_OKAY;
+}
+
+
+
+
+/** deinitialization method of relaxator (called before transformed problem is freed) */
+#if 0
+static
+SCIP_DECL_RELAXEXIT(relaxExitlagr)
+{  /*lint --e{715}*/
+   SCIPerrorMessage("method of lagr relaxator not implemented yet\n");
+   SCIPABORT(); /*lint --e{527}*/
+
+   return SCIP_OKAY;
+}
+#else
+#define relaxExitlagr NULL
+#endif
+
+
+/** solving process initialization method of relaxator (called when branch and bound process is about to begin) */
+#if 0
+static
+SCIP_DECL_RELAXINITSOL(relaxInitsollagr)
+{  /*lint --e{715}*/
+   SCIPerrorMessage("method of lagr relaxator not implemented yet\n");
+   SCIPABORT(); /*lint --e{527}*/
+
+   return SCIP_OKAY;
+}
+#else
+#define relaxInitsollagr NULL
+#endif
+
+
+/** solving process deinitialization method of relaxator (called before branch and bound process data is freed) */
+#if 0
+static
+SCIP_DECL_RELAXEXITSOL(relaxExitsollagr)
+{  /*lint --e{715}*/
+   printf("hellow\n");
+
+   
+   return SCIP_OKAY;
+
+}
+#else
+#define relaxExitsollagr NULL
+#endif
+
+
+/** execution method of relaxator */
+static
+SCIP_DECL_RELAXEXEC(relaxExeclagr)
+{  
+   SCIP* relaxscip;
+   SCIP_HASHMAP* varmap;
+   SCIP_HASHMAP* consmap;
+   SCIP_CONS** conss;
+   SCIP_PROBDATA* probdata;
+   SCIP_VARDATA* vardata;
+
+   SCIP_Real relaxval;
+   SCIP_Bool valid;
+   int nconss;
+   int i;
+   int counter;
+   int id;
+
+
+   // *lowerbound = -SCIPinfinity(scip);
+   // *result = SCIP_DIDNOTRUN;
+
+   /* we can only run if none of the present constraints expect their variables to be binary or integer during transformation */
+   conss = SCIPgetConss(scip);
+   nconss = SCIPgetNConss(scip);
+
+   /* create the variable mapping hash map */
+   SCIP_CALL( SCIPcreate(&relaxscip) );
+   SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)) );
+   valid = FALSE;
+   SCIP_CALL( SCIPcopy(scip, relaxscip, varmap, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &valid) );
+   
+   /**************************************************************************************************************/
+   /*First,                                                                                                      */
+   //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
+   /***************************************************************************************************************/
+   int nvars = SCIPgetNVars(relaxscip);
+   SCIP_VAR** vars = SCIPgetVars(relaxscip);
+   SCIP_VAR** varbuffers;
+   int* badconss;
+   
+   SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
+   int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
+   int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
+   int* listnconsvars = SCIPlistnconsvars(probdata);
+   int* listconsvarids = SCIPlistconsvarids(probdata);
+
+   /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
+   FILE* TimeCollector;
+   TimeCollector = fopen("time.txt","w");
+   SCIP_CLOCK* varslottime;                 //to help us record the time
+   SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
+   SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
+
+   // int nconsvars=0;
+   int* consids;
+
+   SCIP_Real* weights;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
+
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
+
+   for (int v = 0; v < nvars; v++)
+   {
+      SCIP_VAR* var = vars[v];
+      weights[v]=SCIPvarGetObj(var);
+   }
+
+   for (int v = 0; v < nvars; v++)
+   { 
+      int* varids;
+      int NVarInBadConss=0;
+      int nconsvars = 0;
+      SCIP_VAR* var = vars[v];
+
+      int varindex = SCIPvarGetIndex(var);                                    /* (2) */
+      assert(varindex!= NULL);
+
+      // printf("%s****%d\n",SCIPvarGetName(var),varindex);
+      for (int r = 0; r < nSlotConss; ++r)
+      {
+         id = badconss[r];
+         SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+         // printf("%s \t",SCIPconsGetName(cons));
+         SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
+         SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
+         if (!valid){
+         abort(); }
+
+         for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+         {
+            SCIP_VAR* varx = varbuffers[j];
+            int varbufindex = SCIPvarGetIndex(varx);
+            assert(varbufindex != NULL);
+            // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
+            
+            
+            /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+            if (varindex == varbufindex)                                           /* (9) */
+            {
+               
+               // VarSlotConss[NVarInBadConss] = cons;
+               consids[NVarInBadConss]=id;
+               NVarInBadConss++;
+               // printf(" %s \t,",SCIPconsGetName(cons));
+            }
+         }
+      }
+
+      SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
+      for(int t=0;t<NVarInBadConss;t++)
+      {
+         varids[t]=consids[t];
+         // printf("%d \t",varids[t]);
+      }
+
+      // vardata=SCIPvarGetData(var);
+      SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
+      vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
+      vardata->varids = varids;
+      // /**set the variable data to the variable*/
+      SCIPvarSetData(var,vardata);  
+   }
+
+   // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   
+
+   FILE* AfterPreProcessing;
+   AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
+
+   // SCIP_CALL(SCIPprintOrigProḅlem(relaxscip, AfterPreProcessing, "lp", FALSE));
+
+   SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   for(int r=0;r<nSlotConss;r++)
+   {
+      id = badconss[r];
+      SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+      SCIP_CALL(SCIPdelCons(relaxscip,cons));
+   }
+
+   /******************************************************************************************************************/
+   /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
+   /* In the end, we will subtract this sum from the objective of the function.                                      */
+   /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
+   /******************************************************************************************************************/
+   SCIP_Real* dualmultipliers;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
+   
+   SCIP_Real* subgradients;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
+   //initialize subgradients;
+   SCIP_Real stepsize = 1.00000;
+   SCIP_Real sumofduals=0;
+   for ( int r = 0; r < nSlotConss; ++r)
+   {
+
+      dualmultipliers[r] = 0;
+      sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+      
+   }
+
+
+
+   /*******************************************************************************************************/
+   /* The reformulation of the problem can be written as follows                                          */
+   //*>>>>>>>>>>>>>>>>>> min sum { (w[i]+sum{dual[j]})}x[i]-sum{dual[r]} <<<<<<<<<<<<                     */
+   /*where i is nvars, j is NVarInBadConss, and r is nSlotConss for our case *******************************/
+   /****************************************************************************************************************/
+   /* The following function will add the following to the obj(weight) of the variable,                            */
+   //*  the obj(weight) of var + the sum of the dualmultipliers of bad constraints which contains this variable    */
+   /****************************************************************************************************************/
+  
+ 
+   FILE* solutions;
+   solutions = fopen("sol.txt","wr");
+   FILE* dual;
+   dual= fopen("dual.txt","wr");
+   FILE* variableinfo; 
+   variableinfo = fopen("var.txt","wr");
+   FILE* subgrad;
+   subgrad = fopen("subgrads.txt","wr");
+   FILE* varobjects;
+   varobjects=fopen("varobjs.txt","wr");
+   FILE* lower;
+   lower=fopen("lowerbounds.txt","wr");
+   
+
+   int maxiter=50;
+   fprintf(lower,"%d\n",maxiter);
+
+   for(int iter=1;iter<=maxiter;iter++)
+   {
+      
+      for(int v=0;v<nvars;v++)
+      {
+         SCIP_VAR* var = vars[v];
+         double sum =SCIPvarGetObj(var);
+         
+         vardata=SCIPvarGetData(var);
+         int* varids = SCIPvardataGetvarids(vardata); 
+         int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+
+         // printf("\n");
+         for(int t=0;t<NVarInBadConss;t++)
+         {
+            // printf("sum = %f, varid %d, dual %f, ", sum, varids[t],dualmultipliers[varids[t]]);
+            sum += dualmultipliers[varids[t]];
+            // fprintf(varobjects,"{%d, %f, %f\t",varids[t], dualmultipliers[varids[t]],sum);
+         }
+         // fprintf(varobjects,"}\n\n");
+         SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
+         // if(sum>weights[v]){printf("new weight %f",SCIPvarGetObj(var));}
+         
+      }
+      // printf("weight for v1 %f \t:= conss",solvals[1]);
+      // for(int s=0; s<listnconsvars[0];++s)
+      // {
+      //    int id = listconsvarids[s];
+      
+      //    printf("(%s, duals = %f) \t",SCIPconsGetName(SCIPgetConss(scip)[id]), dualmultipliers[id]);
+      // }
+      
+      SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+      
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
+      // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+      SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
+      // fclose(AfterPreProcessing);
+
+      SCIP_CALL( SCIPtransformProb(relaxscip) );
+      SCIP_CALL( SCIPsolve(relaxscip) );
+      relaxval = SCIPgetPrimalbound(relaxscip);
+      // printf("\ndualbound %f, primalbound %f \n",SCIPgetDualbound(relaxscip),SCIPgetPrimalbound(relaxscip));
+      SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
+      /*get the best solution*/   
+      SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
+      SCIP_SOL** sols = SCIPgetSols(relaxscip);
+      int nsols = SCIPgetNSols(relaxscip);
+
+      SCIP_Real* solvals;
+      SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+1)); 
+   
+
+      /*text output*/
+      SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+      fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
+      // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
+
+      /*store the solution in solvals so we can later export it to subgradient function*/
+      SCIP_Real lowerbound=0;
+      SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
+      SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
+
+      SCIP_Real compare=0;
+      for (int v = 0; v<nvars; ++v)
+      {
+         compare += solvals[v]*weights[v]; 
+      }
+
+      printf("compare value %f\n",compare);
+      // for(int s=0;s<nsols;s++)
+      // {
+      //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
+      //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
+      //    SCIP_Real compare=0;
+      //    for (int v = 0; v<nvars; ++v)
+      //    {
+      //       compare += solvals[v]*weights[v]; 
+      //    }
+         
+      //    printf("compare value %f\n",compare);
+      //    if(compare>lowerbound){lowerbound==compare;} 
+      // }
+      // fprintf(dual,"now comes the biggest one\n");
+
+      // for(int s=0;s<nsols;s++)
+      // {
+      //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
+      //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
+      //    SCIP_Real compare=0;
+      //    for (int v = 0; v<nvars; ++v)
+      //    {
+      //       compare += solvals[v]*weights[v]; 
+      //    }
+      //    if(compare==lowerbound){break;} 
+      // }
+      
+      
+
+      stepsize = 15000/double(iter+1); 
+      // fprintf(solutions, "\niteration %d\n",iter);
+      // fprintf(dual, "\niteration %d\n",iter);
+      // fprintf(variableinfo, "\niteration %d\n",iter);
+      // fprintf(varobjects, "\niteration %d\n",iter);
+
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
+      // SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      // SCIP_CALL( SCIPtransformProb(relaxscip) );
+
+      counter = 0;
+      int checker = 0;
+      for(int r=0; r<nSlotConss;++r)
+      {
+         id = badconss[r];
+         double ax=-1;
+         for(int s=counter;s<(counter+listnconsvars[r]);++s)
+         {
+            // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
+            ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
+            // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
+            
+         }
+         
+         counter += listnconsvars[r];
+         if(ax>0){checker++;}
+         subgradients[r]=ax;
+         // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
+         
+      }
+      if(checker==0){printf("#*#*#*result found\n"); break;}
+
+      SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      SCIP_CALL( SCIPtransformProb(relaxscip) );
+   
+      
+
+      
+      for (int v = 0; v<nvars; ++v)
+      {
+         SCIP_VAR* var = vars[v];
+         
+         SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v])); 
+         // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
+         lowerbound += solvals[v]*weights[v]; 
+      }
+      fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
+      fprintf(lower,"%f\n",lowerbound);
+
+      // stepsize = (SCIPgetPrimalbound(relaxscip)-lowerbound)/getnorm(subgradients,nSlotConss,stepsize);
+      SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      fprintf(solutions, "lowerbound = %f \n ", lowerbound);
+      SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
+      
+      //add back the sum of the duals we subtracted from the main obj function
+
+      int sum=0;
+      sumofduals = 0;
+
+      for(int r=0; r<nSlotConss;++r)
+      { 
+         dualmultipliers[r] += subgradients[r]*stepsize;
+         if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
+         
+         sum+=dualmultipliers[r];
+         // fprintf(dual," then %f step size %f \n",dualmultipliers[r], stepsize);
+      }
+      sumofduals=sum;
+      // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
+      SCIPinfoMessage(relaxscip, TimeCollector, "\n new dual found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+      // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
+   }
+   SCIPfreeTransform(relaxscip);
+   fclose(variableinfo);
+   fclose(dual);
+   fclose(subgrad);
+   fclose(varobjects);
+   fclose(solutions);
+   fclose(lower);
+
+   if( SCIPgetStatus(relaxscip) == SCIP_STATUS_OPTIMAL )
+   {
+      /* store relaxation solution in original SCIP if it improves the best relaxation solution thus far */
+      if( (! SCIPisRelaxSolValid(scip)) || SCIPisGT(scip, relaxval, SCIPgetRelaxSolObj(scip)) )
+      {
+         SCIPdebugMsg(scip, "Setting LP relaxation solution, which improved upon earlier solution\n");
+
+
+         SCIP_CALL( SCIPclearRelaxSolVals(scip, relax) );
+
+         for( i = 0; i < SCIPgetNVars(scip); ++i )
+         {
+            SCIP_VAR* relaxvar;
+            SCIP_Real solval;
+
+            relaxvar = (SCIP_VAR*)SCIPhashmapGetImage(varmap, SCIPgetVars(scip)[i]);
+            assert(relaxvar != NULL);
+
+            solval = SCIPgetSolVal(relaxscip, SCIPgetBestSol(relaxscip), relaxvar);
+
+            SCIP_CALL( SCIPsetRelaxSolVal(scip, relax, SCIPgetVars(scip)[i], solval) );
+         }
+
+         /* mark relaxation solution to be valid and inform SCIP that the relaxation included all LP rows */
+         SCIP_CALL( SCIPmarkRelaxSolValid(scip, relax, TRUE) );
+      }
+
+      SCIPdebugMsg(scip, "LP lower bound = %g\n", relaxval);
+
+      *lowerbound = relaxval;
+      *result = SCIP_SUCCESS;
+   }
+   else if( SCIPgetStatus(relaxscip) == SCIP_STATUS_INFEASIBLE )
+   {
+      SCIPdebugMsg(scip, "cutting off node\n");
+      *result = SCIP_CUTOFF;
+   }
+
+   /* free memory */
+   SCIPhashmapFree(&varmap);
+   SCIP_CALL( SCIPfree(&relaxscip) );
+   return SCIP_OKAY;
+}
+
+
+
+
+
+
+/*
+ * relaxator specific interface methods
+ */
+
+/** creates the lagr relaxator and includes it in SCIP */
+SCIP_RETCODE SCIPincludeRelaxlagrangian(
+   SCIP*                 scip                /**< SCIP data structure */
+   )
+{
+   SCIP_RELAXDATA* relaxdata;
+   SCIP_RELAX* relax;
+
+   /* create lagr relaxator data */
+   SCIP_CALL(SCIPallocMemory(scip, &relaxdata));
+   relaxdata = NULL;
+   /* TODO: (optional) create relaxator specific data here */
+
+   relax = NULL;
+
+   /* include relaxator */
+#if 0
+   /* use SCIPincludeRelax() if you want to set all callbacks explicitly and realize (by getting compiler errors) when
+    * new callbacks are added in future SCIP versions
+    */
+   SCIP_CALL( SCIPincludeRelax(scip, RELAX_NAME, RELAX_DESC, RELAX_PRIORITY, RELAX_FREQ, RELAX_INCLUDESLP,
+         relaxCopylagr, relaxFreelagr, relaxInitlagr, relaxExitlagr, relaxInitsollagr, relaxExitsollagr, relaxExeclagr,
+         relaxdata) );
+#else
+   /* use SCIPincludeRelaxBasic() plus setter functions if you want to set callbacks one-by-one and your code should
+    * compile independent of new callbacks being added in future SCIP versions
+    */
+   SCIP_CALL( SCIPincludeRelaxBasic(scip, &relax, RELAX_NAME, RELAX_DESC, RELAX_PRIORITY, RELAX_FREQ,
+         relaxExeclagr, relaxdata) );
+
+   assert(relax != NULL);
+
+   /* set non fundamental callbacks via setter functions */
+   // SCIP_CALL( SCIPsetRelaxCopy(scip, relax, relaxCopylagr) );
+   SCIP_CALL( SCIPsetRelaxFree(scip, relax, relaxFreelagr) );
+   SCIP_CALL( SCIPsetRelaxInit(scip, relax, relaxInitlagr) );
+   SCIP_CALL( SCIPsetRelaxExit(scip, relax, relaxExitlagr) );
+   SCIP_CALL( SCIPsetRelaxInitsol(scip, relax, relaxInitsollagr) );
+   SCIP_CALL( SCIPsetRelaxExitsol(scip, relax, relaxExitsollagr) );
+#endif
+
+   /* add lagr relaxator parameters */
+   /* TODO: (optional) add relaxator specific parameters with SCIPaddTypeParam() here */
+
+   return SCIP_OKAY;
+}
diff --git a/src/relax_lagr.cpp b/src/relax_lagr.cpp
index 8ae0d5ab78c9caa0b3e758c5f36416e5155d11ab..dcc27e4dcc6fe23462e4560089a84088c06cfeda 100644
--- a/src/relax_lagr.cpp
+++ b/src/relax_lagr.cpp
@@ -110,169 +110,171 @@ int* SCIPvardataGetvarids(
  }
 
 
+
 static
 SCIP_DECL_RELAXINIT(relaxInitlagr)
 {  /*lint --e{715}*/
    
-   // SCIP* relaxscip;
-   // SCIP_HASHMAP* varmap;
-   // SCIP_HASHMAP* consmap;
-   // SCIP_CONS** conss;
-   // SCIP_PROBDATA* probdata;
-   // SCIP_VARDATA* vardata;
-
-   // SCIP_Real relaxval;
-   // SCIP_Bool valid;
-   // int nconss;
-   // int i;
-   // int counter;
-   // int id;
-
-
-   // // *lowerbound = -SCIPinfinity(scip);
-   // // *result = SCIP_DIDNOTRUN;
-
-   // /* we can only run if none of the present constraints expect their variables to be binary or integer during transformation */
-   // conss = SCIPgetConss(scip);
-   // nconss = SCIPgetNConss(scip);
-
-   // /* create the variable mapping hash map */
-   // SCIP_CALL( SCIPcreate(&relaxscip) );
-   // SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)) );
-   // valid = FALSE;
-   // SCIP_CALL( SCIPcopy(scip, relaxscip, varmap, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &valid) );
-   
-   // /**************************************************************************************************************/
-   // /*First,                                                                                                      */
-   // //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
-   // /***************************************************************************************************************/
-   // int nvars = SCIPgetNVars(relaxscip);
-   // SCIP_VAR** vars = SCIPgetVars(relaxscip);
-   // SCIP_VAR** varbuffers;
-   // int* badconss;
+   SCIP* relaxscip;
+   SCIP_HASHMAP* varmap;
+   SCIP_HASHMAP* consmap;
+   SCIP_CONS** conss;
+   SCIP_PROBDATA* probdata;
+   SCIP_VARDATA* vardata;
+
+   SCIP_Real relaxval;
+   SCIP_Bool valid;
+   int nconss;
+   int i;
+   int counter;
+   int id;
+
+
+   // *lowerbound = -SCIPinfinity(scip);
+   // *result = SCIP_DIDNOTRUN;
+
+   /* we can only run if none of the present constraints expect their variables to be binary or integer during transformation */
+   conss = SCIPgetConss(scip);
+   nconss = SCIPgetNConss(scip);
+
+   /* create the variable mapping hash map */
+   SCIP_CALL( SCIPcreate(&relaxscip) );
+   SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)) );
+   valid = FALSE;
+   SCIP_CALL( SCIPcopy(scip, relaxscip, varmap, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &valid) );
+ 
+   /**************************************************************************************************************/
+   /*First,                                                                                                      */
+   //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
+   /***************************************************************************************************************/
+   int nvars = SCIPgetNVars(relaxscip);
+   SCIP_VAR** vars = SCIPgetVars(relaxscip);
+   SCIP_VAR** varbuffers;
+   int* badconss;
+
+   SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
+   int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
+   int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
+   int* listnconsvars = SCIPlistnconsvars(probdata);
+   int* listconsvarids = SCIPlistconsvarids(probdata);
+
+   /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
+   FILE* TimeCollector;
+   TimeCollector = fopen("time.txt","w");
+   SCIP_CLOCK* varslottime;                 //to help us record the time
+   SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
+   SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
+
+   SCIP_CLOCK* totaliteration;                 //to help us record the time
+   SCIP_CALL( SCIPcreateClock(relaxscip, &totaliteration) );                     //* start time counting*  
    
-   // SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
-   // int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
-   // int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
-   // int* listnconsvars = SCIPlistnconsvars(probdata);
-   // int* listconsvarids = SCIPlistconsvarids(probdata);
 
-   // /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
-   // FILE* TimeCollector;
-   // TimeCollector = fopen("time.txt","w");
-   // SCIP_CLOCK* varslottime;                 //to help us record the time
-   // SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
-   // SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
 
-   // // int nconsvars=0;
-   // int* consids;
 
-   // SCIP_Real* weights;
-   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
+   int* consids;
 
-   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
+   SCIP_Real* weights;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
 
-   // for (int v = 0; v < nvars; v++)
-   // {
-   //    SCIP_VAR* var = vars[v];
-   //    weights[v]=SCIPvarGetObj(var);
-   // }
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
 
-   // for (int v = 0; v < nvars; v++)
-   // { 
-   //    int* varids;
-   //    int NVarInBadConss=0;
-   //    int nconsvars = 0;
-   //    SCIP_VAR* var = vars[v];
+   SCIP_Real maxobj=0;
+   for (int v = 0; v < nvars; v++)
+   {
+      SCIP_VAR* var = vars[v];
+      weights[v]=SCIPvarGetObj(var);
+      if(maxobj<weights[v]){maxobj=weights[v];}
+   }
+   
+   for (int v = 0; v < nvars; v++)
+   { 
+      int* varids;
+      int NVarInBadConss=0;
+      int nconsvars = 0;
+      SCIP_VAR* var = vars[v];
 
-   //    int varindex = SCIPvarGetIndex(var);                                    /* (2) */
-   //    assert(varindex!= NULL);
+      int varindex = SCIPvarGetIndex(var);                                    /* (2) */
+      assert(varindex!= NULL);
 
-   //    // printf("%s****%d\n",SCIPvarGetName(var),varindex);
-   //    for (int r = 0; r < nSlotConss; ++r)
-   //    {
-   //       id = badconss[r];
-   //       SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
-   //       // printf("%s \t",SCIPconsGetName(cons));
-   //       SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
-   //       SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
-   //       if (!valid){
-   //       abort(); }
+      for (int r = 0; r < nSlotConss; ++r)
+      {
+         id = badconss[r];
+         SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+         // printf("%s \t",SCIPconsGetName(cons));
+         SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
+         SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
+         if (!valid){abort(); }
 
-   //       for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
-   //       {
-   //          SCIP_VAR* varx = varbuffers[j];
-   //          int varbufindex = SCIPvarGetIndex(varx);
-   //          assert(varbufindex != NULL);
-   //          // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
+         for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+         {
+            SCIP_VAR* varx = varbuffers[j];
+            int varbufindex = SCIPvarGetIndex(varx);
+            assert(varbufindex != NULL);
+            // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
             
             
-   //          /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
-   //          if (varindex == varbufindex)                                           /* (9) */
-   //          {
+            /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+            if (varindex == varbufindex)                                           /* (9) */
+            {
                
-   //             // VarSlotConss[NVarInBadConss] = cons;
-   //             consids[NVarInBadConss]=id;
-   //             NVarInBadConss++;
-   //             // printf(" %s \t,",SCIPconsGetName(cons));
-   //          }
-   //       }
-   //    }
+               // VarSlotConss[NVarInBadConss] = cons;
+               consids[NVarInBadConss]=id;
+               NVarInBadConss++;
+               // printf(" %s \t,",SCIPconsGetName(cons));
+            }
+         }
+      }
 
-   //    SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
-   //    for(int t=0;t<NVarInBadConss;t++)
-   //    {
-   //       varids[t]=consids[t];
-   //       // printf("%d \t",varids[t]);
-   //    }
+      SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
+      for(int t=0;t<NVarInBadConss;t++)
+      {
+         varids[t]=consids[t];
+         // printf("%d \t",varids[t]);
+      }
 
    //    // vardata=SCIPvarGetData(var);
-   //    SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
-   //    SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
-   //    vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
-   //    vardata->varids = varids;
-   //    // /**set the variable data to the variable*/
-   //    SCIPvarSetData(var,vardata);  
-   // }
+      SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
+      vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
+      vardata->varids = varids;
+      // /**set the variable data to the variable*/
+      SCIPvarSetData(var,vardata);  
+   }
 
-   // // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
    
 
-   // FILE* AfterPreProcessing;
-   // AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
+   FILE* AfterPreProcessing;
+   AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
 
-   // // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+   // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
 
-   // SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-   // for(int r=0;r<nSlotConss;r++)
-   // {
-   //    id = badconss[r];
-   //    SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
-   //    SCIP_CALL(SCIPdelCons(relaxscip,cons));
-   // }
+   SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   for(int r=0;r<nSlotConss;r++)
+   {
+      id = badconss[r];
+      SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+      SCIP_CALL(SCIPdelCons(relaxscip,cons));
+   }
 
-   // /******************************************************************************************************************/
-   // /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
-   // /* In the end, we will subtract this sum from the objective of the function.                                      */
-   // /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
-   // /******************************************************************************************************************/
-   // SCIP_Real* dualmultipliers;
-   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
+   /******************************************************************************************************************/
+   /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
+   /* In the end, we will subtract this sum from the objective of the function.                                      */
+   /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
+   /******************************************************************************************************************/
+   SCIP_Real* dualmultipliers;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
    
-   // SCIP_Real* subgradients;
-   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
-   // //initialize subgradients;
-   // SCIP_Real stepsize = 150.00000;
-   // SCIP_Real sumofduals=0;
-   // for ( int r = 0; r < nSlotConss; ++r)
-   // {
-   //    // id = badconss[r];
-   //    // SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
-   //    //if k=1 iteration//
-   //    dualmultipliers[r] = 0;
-   //    sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
-      
-   // }
+   SCIP_Real* subgradients;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
+   //initialize subgradients;
+   SCIP_Real stepsize = 15;
+   SCIP_Real sumofduals=0;
+   for ( int r = 0; r < nSlotConss; ++r)
+   {
+      dualmultipliers[r] = 0;
+      sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+   }
 
 
 
@@ -284,209 +286,228 @@ SCIP_DECL_RELAXINIT(relaxInitlagr)
    // /* The following function will add the following to the obj(weight) of the variable,                            */
    // //*  the obj(weight) of var + the sum of the dualmultipliers of bad constraints which contains this variable    */
    // /****************************************************************************************************************/
-  
- 
-   // FILE* solutions;
-   // solutions = fopen("sol.txt","wr");
-   // FILE* dual;
-   // dual= fopen("dual.txt","wr");
-   // FILE* variableinfo; 
-   // variableinfo = fopen("var.txt","wr");
-   // FILE* subgrad;
-   // subgrad = fopen("subgrads.txt","wr");
-   // FILE* varobjects;
-   // varobjects=fopen("varobjs.txt","wr");
-   // FILE* lower;
-   // lower=fopen("lowerbounds.txt","wr");
    
-
-   // int maxiter=125;
-   // fprintf(lower,"%d\n",maxiter);
-
-   // for(int iter=1;iter<=maxiter;iter++)
-   // {
-      
-   //    for(int v=0;v<nvars;v++)
-   //    {
-   //       SCIP_VAR* var = vars[v];
-   //       double sum =SCIPvarGetObj(var);
+ 
+   FILE* solutions;
+   solutions = fopen("sol.txt","wr");
+   FILE* dual;
+   dual= fopen("dual.txt","wr");
+   FILE* variableinfo; 
+   variableinfo = fopen("var.txt","wr");
+   FILE* subgrad;
+   subgrad = fopen("subgrads.txt","wr");
+   FILE* varobjects;
+   varobjects=fopen("varobjs.txt","wr");
+   FILE* lower;
+   lower=fopen("lowerbounds.txt","wr");
+   FILE* iter;
+   iter=fopen("iter.txt","wr");
+
+
+   //fprintf(lower, "hi");
+   SCIP_Real* solvals;  
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+2)); 
+   solvals[nvars+1]=0;  //for last solutions
+   solvals[nvars]=0; //for best solution
+
+   int maxiter=1500;
+
+   int oscilatecounter=0;
+   int improvementcounter = 0;
+   SCIP_Real oscilator1=0;
+   SCIP_Real oscilator2=0;
+   SCIP_Real forcompare = -1000000000000000000;
+   SCIP_CALL(SCIPstartClock(relaxscip,totaliteration)); 
+   
+   for(int iter=1;iter<=maxiter;iter++)
+   {
+      
+      for(int v=0;v<nvars;v++)
+      {
+         SCIP_VAR* var = vars[v];
+         double sum =0;
          
-   //       vardata=SCIPvarGetData(var);
-   //       int* varids = SCIPvardataGetvarids(vardata); 
-   //       int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+         vardata=SCIPvarGetData(var);
+         int* varids = SCIPvardataGetvarids(vardata); 
+         assert(varids=!NULL);
+         int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+         // if(NVarInBadConss==0){SCIPsetSolVal(relaxscip,) break;}
+         // else
+         // {
+         // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+         // fprintf(varobjects,"%s \n",SCIPvarGetName(var));
+         for(int t=0;t<NVarInBadConss;t++)
+         {
+            sum += dualmultipliers[varids[t]];
+            // fprintf(varobjects,"{id = %d, dual = %f, sum = %f\t",varids[t], dualmultipliers[varids[t]],sum);
+         }
+         // fprintf(varobjects,"}\n\n");
+         SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
 
-   //       // printf("\n");
-   //       for(int t=0;t<NVarInBadConss;t++)
-   //       {
-   //          // printf("sum = %f, varid %d, dual %f, ", sum, varids[t],dualmultipliers[varids[t]]);
-   //          sum += dualmultipliers[varids[t]];
-   //          // fprintf(varobjects,"{%d, %f, %f\t",varids[t], dualmultipliers[varids[t]],sum);
-   //       }
-   //       // fprintf(varobjects,"}\n\n");
-   //       SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
-   //       // if(sum>weights[v]){printf("new weight %f",SCIPvarGetObj(var));}
+         // }
          
-   //    }
-   //    // printf("weight for v1 %f \t:= conss",solvals[1]);
-   //    // for(int s=0; s<listnconsvars[0];++s)
-   //    // {
-   //    //    int id = listconsvarids[s];
-      
-   //    //    printf("(%s, duals = %f) \t",SCIPconsGetName(SCIPgetConss(scip)[id]), dualmultipliers[id]);
-   //    // }
-      
-   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+         
+      }
+
+      SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, totaliteration));
       
-   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
-   //    // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
-   //    SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
-   //    // fclose(AfterPreProcessing);
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
+      //SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+      SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
+      // fclose(AfterPreProcessing);
 
-   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
-   //    SCIP_CALL( SCIPsolve(relaxscip) );
-   //    relaxval = SCIPgetPrimalbound(relaxscip);
-   //    // printf("\ndualbound %f, primalbound %f \n",SCIPgetDualbound(relaxscip),SCIPgetPrimalbound(relaxscip));
-   //    SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
-   //    /*get the best solution*/   
-   //    SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
-   //    SCIP_SOL** sols = SCIPgetSols(relaxscip);
-   //    int nsols = SCIPgetNSols(relaxscip);
+      SCIP_CALL( SCIPtransformProb(relaxscip) );
+      SCIP_CALL( SCIPsolve(relaxscip) );
+      relaxval = SCIPgetPrimalbound(relaxscip);
+      //printf("\ndualbound %f \n",SCIPgetDualbound(relaxscip));
+      fprintf(lower,"%f\n",SCIPgetPrimalbound(relaxscip));
+      SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
 
-   //    SCIP_Real* solvals;
-   //    SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+1)); 
-   
+      /*store the highest lower bound*/      
+      if(solvals[nvars]<SCIPgetPrimalbound(relaxscip)){solvals[nvars]=SCIPgetPrimalbound(relaxscip);}
+      fprintf(variableinfo,"%f\n",solvals[nvars]);
 
-   //    /*text output*/
-   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-   //    fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
-   //    // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
+      
 
-   //    /*store the solution in solvals so we can later export it to subgradient function*/
-   //    SCIP_Real lowerbound=0;
-   //    SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
-   //    SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
+      /*make sure we're not oscilating by adding a counter, which checks the absolute value between the difference of the previous few steps and make sure it's not the same. */
+      oscilator2=abs(solvals[nvars+1]-SCIPgetPrimalbound(relaxscip));
+      if(oscilator1==oscilator2){oscilatecounter++;}
+      else{oscilator1=oscilator2; oscilatecounter==0;}
+      //if(oscilatecounter==5){printf("repetition"); break;}
+      //printf("dprev.sol=%f, current=%f, difference %f, coutner=%d, ",solvals[nvars+1],SCIPgetPrimalbound(relaxscip), oscilator2, oscilatecounter);
+      
+      /*store the solution on the last entry of solvals, so we can compare it in the next round for repetitions*/
+      solvals[nvars+1]=SCIPgetPrimalbound(relaxscip);
 
-   //    SCIP_Real compare=0;
-   //    for (int v = 0; v<nvars; ++v)
-   //    {
-   //       compare += solvals[v]*weights[v]; 
-   //    }
+      /*breaking criteria for iterations*/
+      if(solvals[nvars]>forcompare){forcompare=solvals[nvars]; improvementcounter=0;}
+      else{improvementcounter++;}
+      //if(improvementcounter==10){break; fprintf(variableinfo,"%d\n",iter);}
+      printf("terminator %d",improvementcounter);
 
-   //    printf("compare value %f\n",compare);
-   //    // for(int s=0;s<nsols;s++)
-   //    // {
-   //    //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
-   //    //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
-   //    //    SCIP_Real compare=0;
-   //    //    for (int v = 0; v<nvars; ++v)
-   //    //    {
-   //    //       compare += solvals[v]*weights[v]; 
-   //    //    }
-         
-   //    //    printf("compare value %f\n",compare);
-   //    //    if(compare>lowerbound){lowerbound==compare;} 
-   //    // }
-   //    // fprintf(dual,"now comes the biggest one\n");
-
-   //    // for(int s=0;s<nsols;s++)
-   //    // {
-   //    //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
-   //    //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
-   //    //    SCIP_Real compare=0;
-   //    //    for (int v = 0; v<nvars; ++v)
-   //    //    {
-   //    //       compare += solvals[v]*weights[v]; 
-   //    //    }
-   //    //    if(compare==lowerbound){break;} 
-   //    // }
+      /*get the best solution*/   
+      SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
+      SCIP_SOL** sols = SCIPgetSols(relaxscip);
+      int nsols = SCIPgetNSols(relaxscip);
       
+      //fprintf(lower,"%d iteration \n",iter);
+      for(int n=0; n<nsols; n++)
+      {
+         //SCIP_CALL(SCIPprintSol(relaxscip,sols[n],lower,FALSE));
+      }
       
 
-   //    stepsize = 15000/double(iter+1); 
-   //    // fprintf(solutions, "\niteration %d\n",iter);
-   //    // fprintf(dual, "\niteration %d\n",iter);
-   //    // fprintf(variableinfo, "\niteration %d\n",iter);
-   //    // fprintf(varobjects, "\niteration %d\n",iter);
+      /*text output*/
+      //SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, totaliteration));
+      fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
+      // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
 
-   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
-   //    // SCIP_CALL( SCIPfreeTransform(relaxscip) );
-   //    // SCIP_CALL( SCIPtransformProb(relaxscip) );
+      /*store the solution in solvals so we can later export it to subgradient function*/
+      SCIP_Real lowerbound=0;
+      SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
+      //SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
 
-   //    counter = 0;
-   //    int checker = 0;
-   //    for(int r=0; r<nSlotConss;++r)
-   //    {
-   //       id = badconss[r];
-   //       double ax=-1;
-   //       for(int s=counter;s<(counter+listnconsvars[r]);++s)
-   //       {
-   //          // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
-   //          ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
-   //          // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
+      SCIP_Real compare=0;
+      for (int v = 0; v<nvars; ++v)
+      {
+         compare += solvals[v]*weights[v]; 
+      }
+
+
+
+      //stepsize = (stepsize+iter)/double(iter+1); 
+      // fprintf(solutions, "\niteration %d\n",iter);
+      // fprintf(dual, "\niteration %d\n",iter);
+      // fprintf(variableinfo, "\niteration %d\n",iter);
+      // fprintf(varobjects, "\niteration %d\n",iter);
+
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
+      // SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      // SCIP_CALL( SCIPtransformProb(relaxscip) );
+
+      counter = 0;
+      int checker = 0;
+      for(int r=0; r<nSlotConss;++r)
+      {
+         id = badconss[r];
+         double ax=-1;
+         for(int s=counter;s<(counter+listnconsvars[r]);++s)
+         {
+            // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
+            ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
+            // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
             
-   //       }
+         }
          
-   //       counter += listnconsvars[r];
-   //       if(ax>0){checker++;}
-   //       subgradients[r]=ax;
-   //       // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
+         counter += listnconsvars[r];
+         if(ax>0){checker++;}
+         subgradients[r]=ax;
+         // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
          
-   //    }
-   //    if(checker==0){printf("#*#*#*result found\n"); break;}
+      }
+      /*breaking condition on finding a feasible solution*/
+      if(checker==0){printf("#*#*#*result found\n"); break;}
 
-   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
-   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
+      SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      SCIP_CALL( SCIPtransformProb(relaxscip) );
    
       
 
       
-   //    for (int v = 0; v<nvars; ++v)
-   //    {
-   //       SCIP_VAR* var = vars[v];
+      for (int v = 0; v<nvars; ++v)
+      {
+         SCIP_VAR* var = vars[v];
          
-   //       SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v])); 
-   //       // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
-   //       lowerbound += solvals[v]*weights[v]; 
-   //    }
-   //    fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
-   //    fprintf(lower,"%f\n",lowerbound);
+         SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v]));       
+         
+         // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
+         lowerbound += solvals[v]*weights[v]; 
+      }
+      // fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
+      // fprintf(lower,"%f\n",lowerbound);
 
-   //    // stepsize = (SCIPgetPrimalbound(relaxscip)-lowerbound)/getnorm(subgradients,nSlotConss,stepsize);
-   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
-   //    fprintf(solutions, "lowerbound = %f \n ", lowerbound);
-   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
       
-   //    //add back the sum of the duals we subtracted from the main obj function
+      SCIP_Real difference = 3000000-SCIPgetPrimalbound(relaxscip);
+      if(improvementcounter<5){stepsize = 0.25*(difference)/(getnorm(subgradients,nSlotConss,stepsize)*getnorm(subgradients,nSlotConss,stepsize));}
+      else{stepsize = 0.1*(difference)/(getnorm(subgradients,nSlotConss,stepsize)*getnorm(subgradients,nSlotConss,stepsize));}
+      //fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t stepsize= %f \n" ,SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize), stepsize);
+     
+      SCIP_CALL( SCIPfreeTransform(relaxscip) );
+      //fprintf(solutions, "lowerbound = %f \n ", lowerbound);
+      //SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
+      
+      //add back the sum of the duals we subtracted from the main obj function
 
-   //    int sum=0;
-   //    sumofduals = 0;
+      int sum=0;
+      sumofduals = 0;
 
-   //    for(int r=0; r<nSlotConss;++r)
-   //    { 
-   //       dualmultipliers[r] += subgradients[r]*stepsize;
-   //       if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
+      for(int r=0; r<nSlotConss;++r)
+      { 
+         dualmultipliers[r] += subgradients[r]*stepsize;
+         if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
          
-   //       sum+=dualmultipliers[r];
-   //       // fprintf(dual," then %f step size %f \n",dualmultipliers[r], stepsize);
-   //    }
-   //    sumofduals=sum;
-   //    // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
-   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n new dual found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-   //    // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
-   // }
-   // SCIPfreeTransform(relaxscip);
-   // fclose(variableinfo);
-   // fclose(dual);
-   // fclose(subgrad);
-   // fclose(varobjects);
-   // fclose(solutions);
-   // fclose(lower);
+         sumofduals+=dualmultipliers[r];
+         //fprintf(dual," then dual = %f step size %f, subgradient %f \n",dualmultipliers[r], stepsize,subgradients[r]);
+      }
+      // sumofduals=sum;
+      // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
+      SCIPinfoMessage(relaxscip, TimeCollector, "%f\n", SCIPgetClockTime(relaxscip, totaliteration));
+      // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
+   }
+   SCIPfreeTransform(relaxscip);
+   fclose(variableinfo);
+   fclose(dual);
+   fclose(subgrad);
+   fclose(varobjects);
+   fclose(solutions);
+   fclose(lower);
 
-   
 
    /* free memory */
-   // SCIPhashmapFree(&varmap);
-   // SCIP_CALL( SCIPfree(&relaxscip) );
+   SCIPhashmapFree(&varmap);
+   SCIP_CALL( SCIPfree(&relaxscip) );
+
+   
 
    return SCIP_OKAY;
 }
@@ -544,7 +565,6 @@ SCIP_DECL_RELAXEXITSOL(relaxExitsollagr)
 static
 SCIP_DECL_RELAXEXEC(relaxExeclagr)
 {  
-   /*lint --e{715}*/
    SCIP* relaxscip;
    SCIP_HASHMAP* varmap;
    SCIP_HASHMAP* consmap;
@@ -560,8 +580,8 @@ SCIP_DECL_RELAXEXEC(relaxExeclagr)
    int id;
 
 
-   // *lowerbound = -SCIPinfinity(scip);
-   // *result = SCIP_DIDNOTRUN;
+   *lowerbound = -SCIPinfinity(scip);
+   *result = SCIP_DIDNOTRUN;
 
    /* we can only run if none of the present constraints expect their variables to be binary or integer during transformation */
    conss = SCIPgetConss(scip);
@@ -573,343 +593,347 @@ SCIP_DECL_RELAXEXEC(relaxExeclagr)
    valid = FALSE;
    SCIP_CALL( SCIPcopy(scip, relaxscip, varmap, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &valid) );
    
-   /**************************************************************************************************************/
-   /*First,                                                                                                      */
-   //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
-   /***************************************************************************************************************/
-   int nvars = SCIPgetNVars(relaxscip);
-   SCIP_VAR** vars = SCIPgetVars(relaxscip);
-   SCIP_VAR** varbuffers;
-   int* badconss;
+   // /**************************************************************************************************************/
+   // /*First,                                                                                                      */
+   // //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
+   // /***************************************************************************************************************/
+   // int nvars = SCIPgetNVars(relaxscip);
+   // SCIP_VAR** vars = SCIPgetVars(relaxscip);
+   // SCIP_VAR** varbuffers;
+   // int* badconss;
    
-   SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
-   int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
-   int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
-   int* listnconsvars = SCIPlistnconsvars(probdata);
-   int* listconsvarids = SCIPlistconsvarids(probdata);
+   // SCIPcreateprobdata(relaxscip,&probdata,SCIPgetConss(relaxscip),vars,&varbuffers,&badconss);     /*will be used to identify the # of slot(bad) constraints*/ 
+   // int nSlotConss = SCIPgetNSlotConss(probdata);         //number of bad(slot) constraint
+   // int allnconsvars = SCIPgetallnconsvars(probdata);    //sum of all nconsvars, used for creating later on an array to collect the list of varids in each row
+   // int* listnconsvars = SCIPlistnconsvars(probdata);
+   // int* listconsvarids = SCIPlistconsvarids(probdata);
 
-   /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
-   FILE* TimeCollector;
-   TimeCollector = fopen("time.txt","w");
-   SCIP_CLOCK* varslottime;                 //to help us record the time
-   SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
-   SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
+   // /* we then create the vardata function for each variable, to see at which constraint the variable is found*/
+   // FILE* TimeCollector;
+   // TimeCollector = fopen("time.txt","w");
+   // SCIP_CLOCK* varslottime;                 //to help us record the time
+   // SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
+   // SCIP_CALL(SCIPstartClock(relaxscip,varslottime)); 
 
-   // int nconsvars=0;
-   int* consids;
+   // // int nconsvars=0;
+   // int* consids;
 
-   SCIP_Real* weights;
-   SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
+   // SCIP_Real* weights;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&weights,nvars));   
 
-   SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&consids,nSlotConss));
 
-   for (int v = 0; v < nvars; v++)
-   {
-      SCIP_VAR* var = vars[v];
-      weights[v]=SCIPvarGetObj(var);
-   }
+   // for (int v = 0; v < nvars; v++)
+   // {
+   //    SCIP_VAR* var = vars[v];
+   //    weights[v]=SCIPvarGetObj(var);
+   // }
 
-   for (int v = 0; v < nvars; v++)
-   { 
-      int* varids;
-      int NVarInBadConss=0;
-      int nconsvars = 0;
-      SCIP_VAR* var = vars[v];
+   // for (int v = 0; v < nvars; v++)
+   // { 
+   //    int* varids;
+   //    int NVarInBadConss=0;
+   //    int nconsvars = 0;
+   //    SCIP_VAR* var = vars[v];
 
-      int varindex = SCIPvarGetIndex(var);                                    /* (2) */
-      assert(varindex!= NULL);
+   //    int varindex = SCIPvarGetIndex(var);                                    /* (2) */
+   //    assert(varindex!= NULL);
 
-      // printf("%s****%d\n",SCIPvarGetName(var),varindex);
-      for (int r = 0; r < nSlotConss; ++r)
-      {
-         id = badconss[r];
-         SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
-         // printf("%s \t",SCIPconsGetName(cons));
-         SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
-         SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
-         if (!valid){
-         abort(); }
+   //    // printf("%s****%d\n",SCIPvarGetName(var),varindex);
+   //    for (int r = 0; r < nSlotConss; ++r)
+   //    {
+   //       id = badconss[r];
+   //       SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //       // printf("%s \t",SCIPconsGetName(cons));
+   //       SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid)); 
+   //       SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffers, nconsvars, &valid));
+   //       if (!valid){
+   //       abort(); }
 
-         for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
-         {
-            SCIP_VAR* varx = varbuffers[j];
-            int varbufindex = SCIPvarGetIndex(varx);
-            assert(varbufindex != NULL);
-            // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
+   //       for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+   //       {
+   //          SCIP_VAR* varx = varbuffers[j];
+   //          int varbufindex = SCIPvarGetIndex(varx);
+   //          assert(varbufindex != NULL);
+   //          // printf("%s\t \t%d",SCIPvarGetName(varx),varbufindex);
             
             
-            /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
-            if (varindex == varbufindex)                                           /* (9) */
-            {
+   //          /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+   //          if (varindex == varbufindex)                                           /* (9) */
+   //          {
                
-               // VarSlotConss[NVarInBadConss] = cons;
-               consids[NVarInBadConss]=id;
-               NVarInBadConss++;
-               // printf(" %s \t,",SCIPconsGetName(cons));
-            }
-         }
-      }
+   //             // VarSlotConss[NVarInBadConss] = cons;
+   //             consids[NVarInBadConss]=id;
+   //             NVarInBadConss++;
+   //             // printf(" %s \t,",SCIPconsGetName(cons));
+   //          }
+   //       }
+   //    }
 
-      SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
-      for(int t=0;t<NVarInBadConss;t++)
-      {
-         varids[t]=consids[t];
-         // printf("%d \t",varids[t]);
-      }
+   //    SCIP_CALL(SCIPallocBufferArray(relaxscip, &varids, NVarInBadConss));
+   //    for(int t=0;t<NVarInBadConss;t++)
+   //    {
+   //       varids[t]=consids[t];
+   //       // printf("%d \t",varids[t]);
+   //    }
 
-      // vardata=SCIPvarGetData(var);
-      SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
-      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
-      vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
-      vardata->varids = varids;
-      // /**set the variable data to the variable*/
-      SCIPvarSetData(var,vardata);  
-   }
+   //    // vardata=SCIPvarGetData(var);
+   //    SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+   //    SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, NVarInBadConss));
+   //    vardata->NVarInBadConss = NVarInBadConss;  /**copy nVarConss to VarData */
+   //    vardata->varids = varids;
+   //    // /**set the variable data to the variable*/
+   //    SCIPvarSetData(var,vardata);  
+   // }
 
-   // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   // // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
    
 
-   FILE* AfterPreProcessing;
-   AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
+   // FILE* AfterPreProcessing;
+   // AfterPreProcessing = fopen("AfterPreProcessing.txt","w+");
 
-   // SCIP_CALL(SCIPprintOrigProḅlem(relaxscip, AfterPreProcessing, "lp", FALSE));
+   // // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
 
-   SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-   for(int r=0;r<nSlotConss;r++)
-   {
-      id = badconss[r];
-      SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
-      SCIP_CALL(SCIPdelCons(relaxscip,cons));
-   }
+   // SCIPinfoMessage(relaxscip, TimeCollector, "\n row and column identified in (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   // for(int r=0;r<nSlotConss;r++)
+   // {
+   //    id = badconss[r];
+   //    SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //    SCIP_CALL(SCIPdelCons(relaxscip,cons));
+   // }
 
-   /******************************************************************************************************************/
-   /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
-   /* In the end, we will subtract this sum from the objective of the function.                                      */
-   /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
-   /******************************************************************************************************************/
-   SCIP_Real* dualmultipliers;
-   SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
+   // /******************************************************************************************************************/
+   // /*Next, we will do the initial iteration of finding the dual mulpliers of each slot conss, and their sum(dualsum) */
+   // /* In the end, we will subtract this sum from the objective of the function.                                      */
+   // /* It's initial, because while we would search for more dual multipliers to solve the Lagrangian relaxation       */
+   // /******************************************************************************************************************/
+   // SCIP_Real* dualmultipliers;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));
    
-   SCIP_Real* subgradients;
-   SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
-   //initialize subgradients;
-   SCIP_Real stepsize = 1.00000;
-   SCIP_Real sumofduals=0;
-   for ( int r = 0; r < nSlotConss; ++r)
-   {
-
-      dualmultipliers[r] = 0;
-      sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
-      
-   }
+   // SCIP_Real* subgradients;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,nSlotConss));
+   // //initialize subgradients;
+   // SCIP_Real stepsize = 1.00000;
+   // SCIP_Real sumofduals=0;
+   // for ( int r = 0; r < nSlotConss; ++r)
+   // {
+   //    dualmultipliers[r] = 0;
+   //    sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+   // }
 
 
 
-   /*******************************************************************************************************/
-   /* The reformulation of the problem can be written as follows                                          */
-   //*>>>>>>>>>>>>>>>>>> min sum { (w[i]+sum{dual[j]})}x[i]-sum{dual[r]} <<<<<<<<<<<<                     */
-   /*where i is nvars, j is NVarInBadConss, and r is nSlotConss for our case *******************************/
-   /****************************************************************************************************************/
-   /* The following function will add the following to the obj(weight) of the variable,                            */
-   //*  the obj(weight) of var + the sum of the dualmultipliers of bad constraints which contains this variable    */
-   /****************************************************************************************************************/
-  
- 
-   FILE* solutions;
-   solutions = fopen("sol.txt","wr");
-   FILE* dual;
-   dual= fopen("dual.txt","wr");
-   FILE* variableinfo; 
-   variableinfo = fopen("var.txt","wr");
-   FILE* subgrad;
-   subgrad = fopen("subgrads.txt","wr");
-   FILE* varobjects;
-   varobjects=fopen("varobjs.txt","wr");
-   FILE* lower;
-   lower=fopen("lowerbounds.txt","wr");
+   // /*******************************************************************************************************/
+   // /* The reformulation of the problem can be written as follows                                          */
+   // //*>>>>>>>>>>>>>>>>>> min sum { (w[i]+sum{dual[j]})}x[i]-sum{dual[r]} <<<<<<<<<<<<                     */
+   // /*where i is nvars, j is NVarInBadConss, and r is nSlotConss for our case *******************************/
+   // /****************************************************************************************************************/
+   // /* The following function will add the following to the obj(weight) of the variable,                            */
+   // //*  the obj(weight) of var + the sum of the dualmultipliers of bad constraints which contains this variable    */
+   // /****************************************************************************************************************/
    
+ 
+   // FILE* solutions;
+   // solutions = fopen("sol.txt","wr");
+   // FILE* dual;
+   // dual= fopen("dual.txt","wr");
+   // FILE* variableinfo; 
+   // variableinfo = fopen("var.txt","wr");
+   // FILE* subgrad;
+   // subgrad = fopen("subgrads.txt","wr");
+   // FILE* varobjects;
+   // varobjects=fopen("varobjs.txt","wr");
+   // FILE* lower;
+   // lower=fopen("lowerbounds.txt","wr");
+   // FILE* iter;
+   // lower=fopen("iter.txt","wr");
 
-   int maxiter=50;
-   fprintf(lower,"%d\n",maxiter);
 
-   for(int iter=1;iter<=maxiter;iter++)
-   {
+   // SCIP_Real* solvals;  
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+2)); 
+   // solvals[nvars+1]=0;  //for last solutions
+   // solvals[nvars]=0; //for best solution
+
+   // int maxiter=125;
+
+   // int oscilatecounter=0;
+   // int improvementcounter = 0;
+   // SCIP_Real oscilator1=0;
+   // SCIP_Real oscilator2=0;
+   // SCIP_Real forcompare = 0.000;
+
+   // fprintf(lower,"%d\n",maxiter);
+
+   // for(int iter=1;iter<=maxiter;iter++)
+   // {
       
-      for(int v=0;v<nvars;v++)
-      {
-         SCIP_VAR* var = vars[v];
-         double sum =SCIPvarGetObj(var);
+   //    for(int v=0;v<nvars;v++)
+   //    {
+   //       SCIP_VAR* var = vars[v];
+   //       double sum =0;
          
-         vardata=SCIPvarGetData(var);
-         int* varids = SCIPvardataGetvarids(vardata); 
-         int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+   //       vardata=SCIPvarGetData(var);
+   //       int* varids = SCIPvardataGetvarids(vardata); 
+   //       assert(varids=!NULL);
+   //       int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+   //       if(NVarInBadConss==0){break;}
+   //       else
+   //       {
+   //          SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+   //          fprintf(varobjects,"%s \n",SCIPvarGetName(var));
+   //          for(int t=0;t<NVarInBadConss;t++)
+   //          {
+   //             sum += dualmultipliers[varids[t]];
+   //             fprintf(varobjects,"{id = %d, dual = %f, sum = %f\t",varids[t], dualmultipliers[varids[t]],sum);
+   //          }
+   //          fprintf(varobjects,"}\n\n");
+   //          SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
 
-         // printf("\n");
-         for(int t=0;t<NVarInBadConss;t++)
-         {
-            // printf("sum = %f, varid %d, dual %f, ", sum, varids[t],dualmultipliers[varids[t]]);
-            sum += dualmultipliers[varids[t]];
-            // fprintf(varobjects,"{%d, %f, %f\t",varids[t], dualmultipliers[varids[t]],sum);
-         }
-         // fprintf(varobjects,"}\n\n");
-         SCIP_CALL(SCIPaddVarObj(relaxscip,var,sum));
-         // if(sum>weights[v]){printf("new weight %f",SCIPvarGetObj(var));}
+   //       }
          
-      }
-      // printf("weight for v1 %f \t:= conss",solvals[1]);
-      // for(int s=0; s<listnconsvars[0];++s)
-      // {
-      //    int id = listconsvarids[s];
+         
+   //    }
+
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
       
-      //    printf("(%s, duals = %f) \t",SCIPconsGetName(SCIPgetConss(scip)[id]), dualmultipliers[id]);
-      // }
+   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
+   //    // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
+   //    SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
+   //    // fclose(AfterPreProcessing);
+
+   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
+   //    SCIP_CALL( SCIPsolve(relaxscip) );
+   //    relaxval = SCIPgetPrimalbound(relaxscip);
+   //    printf("\ndualbound %f \n",SCIPgetDualbound(relaxscip));
+   //    fprintf(lower,"%f",SCIPgetPrimalbound(relaxscip));
+   //    SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
+
+   //    /*store the highest lower bound*/      
+   //    if(solvals[nvars]<SCIPgetPrimalbound(relaxscip)){solvals[nvars]=SCIPgetPrimalbound(relaxscip);fprintf(variableinfo,}
+   //    fprintf(variableinfo,"%f\n",solvals[nvars]);
+
       
-      SCIPinfoMessage(relaxscip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+
+   //    /*make sure we're not oscilating by adding a counter, which checks the absolute value between the difference of the previous few steps and make sure it's not the same. */
+   //    oscilator2=abs(solvals[nvars+1]-SCIPgetPrimalbound(relaxscip));
+   //    if(oscilator1==oscilator2){oscilatecounter++;}
+   //    else(oscilator1=oscilator2);
+   //    if(oscilatecounter==5){printf("repetition"); break;}
+   //    printf("dprev.sol=%f, current=%f, difference %f, coutner=%d, ",solvals[nvars+1],SCIPgetPrimalbound(relaxscip), oscilator2, oscilatecounter);
       
-      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
-      // SCIP_CALL(SCIPprintOrigProblem(relaxscip, AfterPreProcessing, "lp", FALSE));
-      SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
-      // fclose(AfterPreProcessing);
+   //    /*store the solution on the last entry of solvals, so we can compare it in the next round for repetitions*/
+   //    solvals[nvars+1]=SCIPgetPrimalbound(relaxscip);
 
-      SCIP_CALL( SCIPtransformProb(relaxscip) );
-      SCIP_CALL( SCIPsolve(relaxscip) );
-      relaxval = SCIPgetPrimalbound(relaxscip);
-      // printf("\ndualbound %f, primalbound %f \n",SCIPgetDualbound(relaxscip),SCIPgetPrimalbound(relaxscip));
-      SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
-      /*get the best solution*/   
-      SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
-      SCIP_SOL** sols = SCIPgetSols(relaxscip);
-      int nsols = SCIPgetNSols(relaxscip);
+   //    /*breaking criteria for iterations*/
+   //    if(solvals[nvars]>forcompare){forcompare=solvals[nvars];}
+   //    else{improvementcounter++;}
+   //    if(improvementcounter==5){break;}
 
-      SCIP_Real* solvals;
-      SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+1)); 
-   
+   //    /*get the best solution*/   
+   //    SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip) ;
+   //    SCIP_SOL** sols = SCIPgetSols(relaxscip);
+   //    int nsols = SCIPgetNSols(relaxscip);
 
-      /*text output*/
-      SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-      fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
-      // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
 
-      /*store the solution in solvals so we can later export it to subgradient function*/
-      SCIP_Real lowerbound=0;
-      SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
-      SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
+      
 
-      SCIP_Real compare=0;
-      for (int v = 0; v<nvars; ++v)
-      {
-         compare += solvals[v]*weights[v]; 
-      }
+   //    /*text output*/
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n first iteration: problem solved after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   //    fprintf(solutions,"number of solutions %d, first iteration \t bound=%f, \t objsol=%f \n",nsols, SCIPgetPrimalbound(relaxscip),relaxval);
+   //    // SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
 
-      printf("compare value %f\n",compare);
-      // for(int s=0;s<nsols;s++)
-      // {
-      //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
-      //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
-      //    SCIP_Real compare=0;
-      //    for (int v = 0; v<nvars; ++v)
-      //    {
-      //       compare += solvals[v]*weights[v]; 
-      //    }
-         
-      //    printf("compare value %f\n",compare);
-      //    if(compare>lowerbound){lowerbound==compare;} 
-      // }
-      // fprintf(dual,"now comes the biggest one\n");
-
-      // for(int s=0;s<nsols;s++)
-      // {
-      //    SCIPgetSolVals(relaxscip,sols[s],nvars,vars,solvals);
-      //    SCIP_CALL(SCIPprintSol(relaxscip,sols[s],dual,FALSE));
-      //    SCIP_Real compare=0;
-      //    for (int v = 0; v<nvars; ++v)
-      //    {
-      //       compare += solvals[v]*weights[v]; 
-      //    }
-      //    if(compare==lowerbound){break;} 
-      // }
-      
-      
+   //    /*store the solution in solvals so we can later export it to subgradient function*/
+   //    SCIP_Real lowerbound=0;
+   //    SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
+   //    SCIP_CALL(SCIPprintSol(relaxscip,bestsol,dual,FALSE));
 
-      stepsize = 15000/double(iter+1); 
-      // fprintf(solutions, "\niteration %d\n",iter);
-      // fprintf(dual, "\niteration %d\n",iter);
-      // fprintf(variableinfo, "\niteration %d\n",iter);
-      // fprintf(varobjects, "\niteration %d\n",iter);
+   //    SCIP_Real compare=0;
+   //    for (int v = 0; v<nvars; ++v)
+   //    {
+   //       compare += solvals[v]*weights[v]; 
+   //    }
 
-      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
-      // SCIP_CALL( SCIPfreeTransform(relaxscip) );
-      // SCIP_CALL( SCIPtransformProb(relaxscip) );
 
-      counter = 0;
-      int checker = 0;
-      for(int r=0; r<nSlotConss;++r)
-      {
-         id = badconss[r];
-         double ax=-1;
-         for(int s=counter;s<(counter+listnconsvars[r]);++s)
-         {
-            // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
-            ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
-            // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
+   //    // stepsize = 15/double(iter+1); 
+   //    // fprintf(solutions, "\niteration %d\n",iter);
+   //    // fprintf(dual, "\niteration %d\n",iter);
+   //    // fprintf(variableinfo, "\niteration %d\n",iter);
+   //    // fprintf(varobjects, "\niteration %d\n",iter);
+
+   //    SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
+   //    // SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    // SCIP_CALL( SCIPtransformProb(relaxscip) );
+
+   //    counter = 0;
+   //    int checker = 0;
+   //    for(int r=0; r<nSlotConss;++r)
+   //    {
+   //       id = badconss[r];
+   //       double ax=-1;
+   //       for(int s=counter;s<(counter+listnconsvars[r]);++s)
+   //       {
+   //          // printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
+   //          ax+=SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]);
+   //          // fprintf(subgrad,"%s\t,%f\t, sum %f",SCIPvarGetName(vars[listconsvarids[s]]),SCIPgetSolVal(relaxscip,bestsol,vars[listconsvarids[s]]),ax);
             
-         }
+   //       }
          
-         counter += listnconsvars[r];
-         if(ax>0){checker++;}
-         subgradients[r]=ax;
-         // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
+   //       counter += listnconsvars[r];
+   //       if(ax>0){checker++;}
+   //       subgradients[r]=ax;
+   //       // fprintf(subgrad, "\n subgrad = %f \t",subgradients[r]);
          
-      }
-      if(checker==0){printf("#*#*#*result found\n"); break;}
+   //    }
+   //    /*breaking condition on finding a feasible solution*/
+   //    if(checker==0){printf("#*#*#*result found\n"); break;}
 
-      SCIP_CALL( SCIPfreeTransform(relaxscip) );
-      SCIP_CALL( SCIPtransformProb(relaxscip) );
+   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    SCIP_CALL( SCIPtransformProb(relaxscip) );
    
       
 
       
-      for (int v = 0; v<nvars; ++v)
-      {
-         SCIP_VAR* var = vars[v];
+   //    for (int v = 0; v<nvars; ++v)
+   //    {
+   //       SCIP_VAR* var = vars[v];
          
-         SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v])); 
-         // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
-         lowerbound += solvals[v]*weights[v]; 
-      }
-      fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
-      fprintf(lower,"%f\n",lowerbound);
+   //       SCIP_CALL(SCIPchgVarObj(relaxscip,var,weights[v])); 
+   //       // fprintf(variableinfo,"(%s,%f,%f)->%f\n",SCIPvarGetName(var),solvals[v],SCIPvarGetObj(var), weights[v]);
+   //       lowerbound += solvals[v]*weights[v]; 
+   //    }
+   //    // fprintf(dual,"dualbound = %f, lowerbound=%f, norm of subgrad %f\t",SCIPgetPrimalbound(relaxscip),lowerbound, getnorm(subgradients,nSlotConss,stepsize));
+   //    // fprintf(lower,"%f\n",lowerbound);
 
-      // stepsize = (SCIPgetPrimalbound(relaxscip)-lowerbound)/getnorm(subgradients,nSlotConss,stepsize);
-      SCIP_CALL( SCIPfreeTransform(relaxscip) );
-      fprintf(solutions, "lowerbound = %f \n ", lowerbound);
-      SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
+   //    // stepsize = (SCIPgetPrimalbound(relaxscip)-lowerbound)/getnorm(subgradients,nSlotConss,stepsize);
+   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );
+   //    fprintf(solutions, "lowerbound = %f \n ", lowerbound);
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n, lowerbound = %f \n", SCIPgetClockTime(relaxscip, varslottime),lowerbound);
       
-      //add back the sum of the duals we subtracted from the main obj function
+   //    //add back the sum of the duals we subtracted from the main obj function
 
-      int sum=0;
-      sumofduals = 0;
+   //    int sum=0;
+   //    sumofduals = 0;
 
-      for(int r=0; r<nSlotConss;++r)
-      { 
-         dualmultipliers[r] += subgradients[r]*stepsize;
-         if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
+   //    for(int r=0; r<nSlotConss;++r)
+   //    { 
+   //       dualmultipliers[r] += subgradients[r]*stepsize;
+   //       if(dualmultipliers[r]<0){dualmultipliers[r]=0;}
          
-         sum+=dualmultipliers[r];
-         // fprintf(dual," then %f step size %f \n",dualmultipliers[r], stepsize);
-      }
-      sumofduals=sum;
-      // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
-      SCIPinfoMessage(relaxscip, TimeCollector, "\n new dual found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
-      // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
-   }
-   SCIPfreeTransform(relaxscip);
-   fclose(variableinfo);
-   fclose(dual);
-   fclose(subgrad);
-   fclose(varobjects);
-   fclose(solutions);
-   fclose(lower);
+   //       sum+=dualmultipliers[r];
+   //       // fprintf(dual," then %f step size %f \n",dualmultipliers[r], stepsize);
+   //    }
+   //    sumofduals=sum;
+   //    // fprintf(dual,"iteration %d, sumofduals=%f\n",iter, sumofduals);
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n new dual found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+   //    // if(checker==0){printf("solution found in %d iterations\n",iter); break;}
+   // }
+   // SCIPfreeTransform(relaxscip);
+   // fclose(variableinfo);
+   // fclose(dual);
+   // fclose(subgrad);
+   // fclose(varobjects);
+   // fclose(solutions);
+   // fclose(lower);
 
    if( SCIPgetStatus(relaxscip) == SCIP_STATUS_OPTIMAL )
    {
diff --git a/time.txt b/time.txt
index 41ac65c33a3dc34a646bcd3e18b812e723296f81..f2b1d97262164ff8c5e6d514d1424b8f06d787a3 100644
--- a/time.txt
+++ b/time.txt
@@ -1,452 +1,1954 @@
 
- row and column identified in (sec) : 0.000055
+ row and column identified in (sec) : 5.892216
 
- finished changing the variable's weight after (sec) : 0.000169
+ finished changing the variable's weight after (sec) : 0.000992
+0.247141
 
- first iteration: problem solved after (sec) : 0.000911
+ finished changing the variable's weight after (sec) : 0.248208
+0.491481
 
- subgradients found after (sec) : 0.001427
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 0.492452
+0.743971
 
- new dual found after (sec) : 0.001440
+ finished changing the variable's weight after (sec) : 0.744951
+1.000979
 
- finished changing the variable's weight after (sec) : 0.001445
+ finished changing the variable's weight after (sec) : 1.001950
+1.262309
 
- first iteration: problem solved after (sec) : 0.002197
+ finished changing the variable's weight after (sec) : 1.263233
+1.525979
 
- subgradients found after (sec) : 0.002898
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 1.526949
+1.793930
 
- new dual found after (sec) : 0.002912
+ finished changing the variable's weight after (sec) : 1.794884
+2.065105
 
- finished changing the variable's weight after (sec) : 0.002917
+ finished changing the variable's weight after (sec) : 2.066069
+2.344470
 
- first iteration: problem solved after (sec) : 0.003969
+ finished changing the variable's weight after (sec) : 2.345411
+2.629939
 
- subgradients found after (sec) : 0.005664
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 2.630915
+2.909456
 
- new dual found after (sec) : 0.005679
+ finished changing the variable's weight after (sec) : 2.910431
+3.189980
 
- finished changing the variable's weight after (sec) : 0.005686
+ finished changing the variable's weight after (sec) : 3.190971
+3.457004
 
- first iteration: problem solved after (sec) : 0.006755
+ finished changing the variable's weight after (sec) : 3.457967
+3.736238
 
- subgradients found after (sec) : 0.007419
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 3.737181
+4.015655
 
- new dual found after (sec) : 0.007432
+ finished changing the variable's weight after (sec) : 4.016587
+4.288782
 
- finished changing the variable's weight after (sec) : 0.007437
+ finished changing the variable's weight after (sec) : 4.289743
+4.561139
 
- first iteration: problem solved after (sec) : 0.008202
+ finished changing the variable's weight after (sec) : 4.562088
+4.843824
 
- subgradients found after (sec) : 0.010177
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 4.844746
+5.125823
 
- new dual found after (sec) : 0.010192
+ finished changing the variable's weight after (sec) : 5.126803
+5.409137
 
- finished changing the variable's weight after (sec) : 0.010197
+ finished changing the variable's weight after (sec) : 5.410105
+5.688909
 
- first iteration: problem solved after (sec) : 0.011938
+ finished changing the variable's weight after (sec) : 5.689848
+5.971025
 
- subgradients found after (sec) : 0.013588
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 5.971956
+6.253414
 
- new dual found after (sec) : 0.013603
+ finished changing the variable's weight after (sec) : 6.254443
+6.535378
 
- finished changing the variable's weight after (sec) : 0.013608
+ finished changing the variable's weight after (sec) : 6.536341
+6.816227
 
- first iteration: problem solved after (sec) : 0.015446
+ finished changing the variable's weight after (sec) : 6.817191
+7.121032
 
- subgradients found after (sec) : 0.017200
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 7.121997
+7.400755
 
- new dual found after (sec) : 0.017216
+ finished changing the variable's weight after (sec) : 7.401698
+7.680134
 
- finished changing the variable's weight after (sec) : 0.017224
+ finished changing the variable's weight after (sec) : 7.681053
+7.959580
 
- first iteration: problem solved after (sec) : 0.019215
+ finished changing the variable's weight after (sec) : 7.960522
+8.235626
 
- subgradients found after (sec) : 0.020945
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 8.236564
+8.512144
 
- new dual found after (sec) : 0.020960
+ finished changing the variable's weight after (sec) : 8.513092
+8.789631
 
- finished changing the variable's weight after (sec) : 0.020964
+ finished changing the variable's weight after (sec) : 8.790588
+9.068874
 
- first iteration: problem solved after (sec) : 0.023213
+ finished changing the variable's weight after (sec) : 9.069795
+9.350248
 
- subgradients found after (sec) : 0.025102
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 9.351185
+9.627496
 
- new dual found after (sec) : 0.025117
+ finished changing the variable's weight after (sec) : 9.628458
+9.907019
 
- finished changing the variable's weight after (sec) : 0.025122
+ finished changing the variable's weight after (sec) : 9.907952
+10.181124
 
- first iteration: problem solved after (sec) : 0.027480
+ finished changing the variable's weight after (sec) : 10.182092
+10.457857
 
- subgradients found after (sec) : 0.029355
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 10.458805
+10.724035
 
- new dual found after (sec) : 0.029371
+ finished changing the variable's weight after (sec) : 10.724966
+11.003525
 
- finished changing the variable's weight after (sec) : 0.029376
+ finished changing the variable's weight after (sec) : 11.004441
+11.285199
 
- first iteration: problem solved after (sec) : 0.031981
+ finished changing the variable's weight after (sec) : 11.286162
+11.565343
 
- subgradients found after (sec) : 0.033981
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 11.566283
+11.843482
 
- new dual found after (sec) : 0.033995
+ finished changing the variable's weight after (sec) : 11.844417
+12.119817
 
- finished changing the variable's weight after (sec) : 0.034000
+ finished changing the variable's weight after (sec) : 12.120757
+12.397025
 
- first iteration: problem solved after (sec) : 0.036610
+ finished changing the variable's weight after (sec) : 12.397941
+12.678396
 
- subgradients found after (sec) : 0.038584
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 12.679328
+12.958683
 
- new dual found after (sec) : 0.038597
+ finished changing the variable's weight after (sec) : 12.959613
+13.236010
 
- finished changing the variable's weight after (sec) : 0.038602
+ finished changing the variable's weight after (sec) : 13.236943
+13.516583
 
- first iteration: problem solved after (sec) : 0.041170
+ finished changing the variable's weight after (sec) : 13.517517
+13.793958
 
- subgradients found after (sec) : 0.043030
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 13.794907
+14.065377
 
- new dual found after (sec) : 0.043043
+ finished changing the variable's weight after (sec) : 14.066328
+14.377505
 
- finished changing the variable's weight after (sec) : 0.043048
+ finished changing the variable's weight after (sec) : 14.378630
+14.652401
 
- first iteration: problem solved after (sec) : 0.045587
+ finished changing the variable's weight after (sec) : 14.653451
+14.928296
 
- subgradients found after (sec) : 0.047498
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 14.929319
+15.211655
 
- new dual found after (sec) : 0.047511
+ finished changing the variable's weight after (sec) : 15.212642
+15.488599
 
- finished changing the variable's weight after (sec) : 0.047516
+ finished changing the variable's weight after (sec) : 15.489565
+15.766486
 
- first iteration: problem solved after (sec) : 0.049942
+ finished changing the variable's weight after (sec) : 15.767436
+16.045703
 
- subgradients found after (sec) : 0.051783
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 16.046673
+16.326163
 
- new dual found after (sec) : 0.051796
+ finished changing the variable's weight after (sec) : 16.327091
+16.604902
 
- finished changing the variable's weight after (sec) : 0.051802
+ finished changing the variable's weight after (sec) : 16.605879
+16.884864
 
- first iteration: problem solved after (sec) : 0.054421
+ finished changing the variable's weight after (sec) : 16.885797
+17.162426
 
- subgradients found after (sec) : 0.056596
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 17.163354
+17.438359
 
- new dual found after (sec) : 0.056613
+ finished changing the variable's weight after (sec) : 17.439292
+17.724774
 
- finished changing the variable's weight after (sec) : 0.056618
+ finished changing the variable's weight after (sec) : 17.725724
+18.021996
 
- first iteration: problem solved after (sec) : 0.059167
+ finished changing the variable's weight after (sec) : 18.023204
+18.352747
 
- subgradients found after (sec) : 0.061021
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 18.353839
+18.725361
 
- new dual found after (sec) : 0.061036
+ finished changing the variable's weight after (sec) : 18.726491
+19.055019
 
- finished changing the variable's weight after (sec) : 0.061041
+ finished changing the variable's weight after (sec) : 19.056101
+19.370904
 
- first iteration: problem solved after (sec) : 0.063553
+ finished changing the variable's weight after (sec) : 19.372054
+19.693206
 
- subgradients found after (sec) : 0.065522
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 19.694316
+19.992940
 
- new dual found after (sec) : 0.065537
+ finished changing the variable's weight after (sec) : 19.993912
+20.330565
 
- finished changing the variable's weight after (sec) : 0.065542
+ finished changing the variable's weight after (sec) : 20.331639
+20.620395
 
- first iteration: problem solved after (sec) : 0.068129
+ finished changing the variable's weight after (sec) : 20.621356
+20.940031
 
- subgradients found after (sec) : 0.070059
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 20.941098
+21.239354
 
- new dual found after (sec) : 0.070076
+ finished changing the variable's weight after (sec) : 21.240376
+21.567214
 
- finished changing the variable's weight after (sec) : 0.070082
+ finished changing the variable's weight after (sec) : 21.568389
+21.924884
 
- first iteration: problem solved after (sec) : 0.072604
+ finished changing the variable's weight after (sec) : 21.926191
+22.258890
 
- subgradients found after (sec) : 0.074551
-, lowerbound = 30.000000 
+ finished changing the variable's weight after (sec) : 22.260123
+22.622424
 
- new dual found after (sec) : 0.074565
+ finished changing the variable's weight after (sec) : 22.623375
+22.923193
 
- finished changing the variable's weight after (sec) : 0.074570
+ finished changing the variable's weight after (sec) : 22.924568
+23.231401
 
- first iteration: problem solved after (sec) : 0.077096
+ finished changing the variable's weight after (sec) : 23.232372
+23.550382
 
- subgradients found after (sec) : 0.078984
-, lowerbound = 1.000000 
+ finished changing the variable's weight after (sec) : 23.551458
+23.904384
 
- new dual found after (sec) : 0.078997
+ finished changing the variable's weight after (sec) : 23.905584
+24.228552
 
- finished changing the variable's weight after (sec) : 0.079002
+ finished changing the variable's weight after (sec) : 24.229580
+24.533670
 
- first iteration: problem solved after (sec) : 0.081496
+ finished changing the variable's weight after (sec) : 24.534682
+24.854162
 
- subgradients found after (sec) : 0.083325
-, lowerbound = 60.000000 
+ finished changing the variable's weight after (sec) : 24.855574
+25.166502
 
- new dual found after (sec) : 0.083339
+ finished changing the variable's weight after (sec) : 25.167504
+25.464516
 
- finished changing the variable's weight after (sec) : 0.083344
+ finished changing the variable's weight after (sec) : 25.465514
+25.760773
 
- first iteration: problem solved after (sec) : 0.085809
+ finished changing the variable's weight after (sec) : 25.761770
+26.053852
 
- subgradients found after (sec) : 0.087656
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 26.054837
+26.345908
 
- new dual found after (sec) : 0.087672
+ finished changing the variable's weight after (sec) : 26.346999
+26.640923
 
- finished changing the variable's weight after (sec) : 0.087680
+ finished changing the variable's weight after (sec) : 26.641882
+26.949889
 
- first iteration: problem solved after (sec) : 0.091279
+ finished changing the variable's weight after (sec) : 26.950956
+27.249144
 
- subgradients found after (sec) : 0.094074
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 27.250159
+27.552353
 
- new dual found after (sec) : 0.094091
+ finished changing the variable's weight after (sec) : 27.553412
+27.855877
 
- finished changing the variable's weight after (sec) : 0.094097
+ finished changing the variable's weight after (sec) : 27.856935
+28.161073
 
- first iteration: problem solved after (sec) : 0.096499
+ finished changing the variable's weight after (sec) : 28.162101
+28.465782
 
- subgradients found after (sec) : 0.098251
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 28.466802
+28.758351
 
- new dual found after (sec) : 0.098263
+ finished changing the variable's weight after (sec) : 28.759347
+29.099824
 
- finished changing the variable's weight after (sec) : 0.098267
+ finished changing the variable's weight after (sec) : 29.100952
+29.487277
 
- first iteration: problem solved after (sec) : 0.100514
+ finished changing the variable's weight after (sec) : 29.488506
+29.797067
 
- subgradients found after (sec) : 0.102194
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 29.798128
+30.088761
 
- new dual found after (sec) : 0.102206
+ finished changing the variable's weight after (sec) : 30.089791
+30.379004
 
- finished changing the variable's weight after (sec) : 0.102211
+ finished changing the variable's weight after (sec) : 30.380002
+30.675321
 
- first iteration: problem solved after (sec) : 0.104544
+ finished changing the variable's weight after (sec) : 30.676301
+30.966540
 
- subgradients found after (sec) : 0.106241
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 30.967535
+31.258406
 
- new dual found after (sec) : 0.106254
+ finished changing the variable's weight after (sec) : 31.259403
+31.554602
 
- finished changing the variable's weight after (sec) : 0.106259
+ finished changing the variable's weight after (sec) : 31.555603
+31.846340
 
- first iteration: problem solved after (sec) : 0.108535
+ finished changing the variable's weight after (sec) : 31.847325
+32.121592
 
- subgradients found after (sec) : 0.110238
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 32.122573
+32.395389
 
- new dual found after (sec) : 0.110251
+ finished changing the variable's weight after (sec) : 32.396314
+32.666812
 
- finished changing the variable's weight after (sec) : 0.110256
+ finished changing the variable's weight after (sec) : 32.667768
+32.938522
 
- first iteration: problem solved after (sec) : 0.112563
+ finished changing the variable's weight after (sec) : 32.939536
+33.205765
 
- subgradients found after (sec) : 0.114254
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 33.206762
+33.472645
 
- new dual found after (sec) : 0.114266
+ finished changing the variable's weight after (sec) : 33.473611
+33.739725
 
- finished changing the variable's weight after (sec) : 0.114270
+ finished changing the variable's weight after (sec) : 33.740687
+34.008941
 
- first iteration: problem solved after (sec) : 0.116537
+ finished changing the variable's weight after (sec) : 34.009930
+34.288004
 
- subgradients found after (sec) : 0.118212
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 34.289007
+34.565125
 
- new dual found after (sec) : 0.118225
+ finished changing the variable's weight after (sec) : 34.566158
+34.833119
 
- finished changing the variable's weight after (sec) : 0.118230
+ finished changing the variable's weight after (sec) : 34.834085
+35.134565
 
- first iteration: problem solved after (sec) : 0.120541
+ finished changing the variable's weight after (sec) : 35.135708
+35.493532
 
- subgradients found after (sec) : 0.122219
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 35.494601
+35.779736
 
- new dual found after (sec) : 0.122231
+ finished changing the variable's weight after (sec) : 35.780703
+36.090842
 
- finished changing the variable's weight after (sec) : 0.122236
+ finished changing the variable's weight after (sec) : 36.092072
+36.438170
 
- first iteration: problem solved after (sec) : 0.124488
+ finished changing the variable's weight after (sec) : 36.439201
+36.704445
 
- subgradients found after (sec) : 0.126186
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 36.705440
+36.973841
 
- new dual found after (sec) : 0.126200
+ finished changing the variable's weight after (sec) : 36.974886
+37.298215
 
- finished changing the variable's weight after (sec) : 0.126204
+ finished changing the variable's weight after (sec) : 37.299417
+37.594601
 
- first iteration: problem solved after (sec) : 0.128470
+ finished changing the variable's weight after (sec) : 37.595614
+37.862530
 
- subgradients found after (sec) : 0.130201
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 37.863584
+38.132876
 
- new dual found after (sec) : 0.130214
+ finished changing the variable's weight after (sec) : 38.133859
+38.412841
 
- finished changing the variable's weight after (sec) : 0.130219
+ finished changing the variable's weight after (sec) : 38.413880
+38.681605
 
- first iteration: problem solved after (sec) : 0.132478
+ finished changing the variable's weight after (sec) : 38.682590
+38.951701
 
- subgradients found after (sec) : 0.134190
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 38.952676
+39.218843
 
- new dual found after (sec) : 0.134203
+ finished changing the variable's weight after (sec) : 39.219834
+39.489177
 
- finished changing the variable's weight after (sec) : 0.134207
+ finished changing the variable's weight after (sec) : 39.490162
+39.756555
 
- first iteration: problem solved after (sec) : 0.136581
+ finished changing the variable's weight after (sec) : 39.757590
+40.024696
 
- subgradients found after (sec) : 0.138378
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 40.025683
+40.317531
 
- new dual found after (sec) : 0.138391
+ finished changing the variable's weight after (sec) : 40.318584
+40.586846
 
- finished changing the variable's weight after (sec) : 0.138395
+ finished changing the variable's weight after (sec) : 40.587871
+40.854038
 
- first iteration: problem solved after (sec) : 0.140776
+ finished changing the variable's weight after (sec) : 40.855087
+41.123339
 
- subgradients found after (sec) : 0.142567
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 41.124299
+41.393662
 
- new dual found after (sec) : 0.142579
+ finished changing the variable's weight after (sec) : 41.394688
+41.662278
 
- finished changing the variable's weight after (sec) : 0.142584
+ finished changing the variable's weight after (sec) : 41.663202
+42.004842
 
- first iteration: problem solved after (sec) : 0.144978
+ finished changing the variable's weight after (sec) : 42.006102
+42.309989
 
- subgradients found after (sec) : 0.146787
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 42.310939
+42.579236
 
- new dual found after (sec) : 0.146800
+ finished changing the variable's weight after (sec) : 42.580234
+42.846331
 
- finished changing the variable's weight after (sec) : 0.146805
+ finished changing the variable's weight after (sec) : 42.847291
+43.115463
 
- first iteration: problem solved after (sec) : 0.149269
+ finished changing the variable's weight after (sec) : 43.116427
+43.383095
 
- subgradients found after (sec) : 0.151069
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 43.384024
+43.649221
 
- new dual found after (sec) : 0.151082
+ finished changing the variable's weight after (sec) : 43.650228
+43.917312
 
- finished changing the variable's weight after (sec) : 0.151087
+ finished changing the variable's weight after (sec) : 43.918286
+44.187236
 
- first iteration: problem solved after (sec) : 0.153523
+ finished changing the variable's weight after (sec) : 44.188173
+44.457303
 
- subgradients found after (sec) : 0.155325
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 44.458292
+44.726083
 
- new dual found after (sec) : 0.155339
+ finished changing the variable's weight after (sec) : 44.727074
+44.992682
 
- finished changing the variable's weight after (sec) : 0.155348
+ finished changing the variable's weight after (sec) : 44.993676
+45.260723
 
- first iteration: problem solved after (sec) : 0.157723
+ finished changing the variable's weight after (sec) : 45.261657
+45.529810
 
- subgradients found after (sec) : 0.159380
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+ finished changing the variable's weight after (sec) : 45.530776
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- new dual found after (sec) : 0.159391
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- finished changing the variable's weight after (sec) : 0.159396
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- first iteration: problem solved after (sec) : 0.161742
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- subgradients found after (sec) : 0.163503
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- new dual found after (sec) : 0.163515
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- finished changing the variable's weight after (sec) : 0.163520
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- first iteration: problem solved after (sec) : 0.166007
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- subgradients found after (sec) : 0.167764
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+ finished changing the variable's weight after (sec) : 47.690815
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- new dual found after (sec) : 0.167775
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- finished changing the variable's weight after (sec) : 0.167780
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- first iteration: problem solved after (sec) : 0.170172
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- subgradients found after (sec) : 0.172208
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+ finished changing the variable's weight after (sec) : 48.772529
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- new dual found after (sec) : 0.172226
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- finished changing the variable's weight after (sec) : 0.172234
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- first iteration: problem solved after (sec) : 0.174879
+ finished changing the variable's weight after (sec) : 49.669126
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- subgradients found after (sec) : 0.176646
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+ finished changing the variable's weight after (sec) : 49.933485
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- new dual found after (sec) : 0.176658
+ finished changing the variable's weight after (sec) : 50.206258
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- finished changing the variable's weight after (sec) : 0.176663
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- first iteration: problem solved after (sec) : 0.179005
+ finished changing the variable's weight after (sec) : 50.739161
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- subgradients found after (sec) : 0.180793
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+ finished changing the variable's weight after (sec) : 51.007468
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- new dual found after (sec) : 0.180806
+ finished changing the variable's weight after (sec) : 51.287033
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- finished changing the variable's weight after (sec) : 0.180811
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- first iteration: problem solved after (sec) : 0.183271
+ finished changing the variable's weight after (sec) : 51.843956
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- subgradients found after (sec) : 0.185071
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+ finished changing the variable's weight after (sec) : 52.126485
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- new dual found after (sec) : 0.185084
+ finished changing the variable's weight after (sec) : 52.402634
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- finished changing the variable's weight after (sec) : 0.185089
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- first iteration: problem solved after (sec) : 0.187422
+ finished changing the variable's weight after (sec) : 52.964105
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- subgradients found after (sec) : 0.189211
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+ finished changing the variable's weight after (sec) : 53.243542
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- new dual found after (sec) : 0.189223
+ finished changing the variable's weight after (sec) : 53.530624
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- finished changing the variable's weight after (sec) : 0.189228
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- first iteration: problem solved after (sec) : 0.191547
+ finished changing the variable's weight after (sec) : 54.109171
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- subgradients found after (sec) : 0.193296
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+ finished changing the variable's weight after (sec) : 54.390672
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- new dual found after (sec) : 0.193309
+ finished changing the variable's weight after (sec) : 54.672144
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- finished changing the variable's weight after (sec) : 0.193313
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- first iteration: problem solved after (sec) : 0.195583
+ finished changing the variable's weight after (sec) : 55.237622
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- subgradients found after (sec) : 0.197289
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 55.517665
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- new dual found after (sec) : 0.197303
+ finished changing the variable's weight after (sec) : 55.794417
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- finished changing the variable's weight after (sec) : 0.197307
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- first iteration: problem solved after (sec) : 0.199632
+ finished changing the variable's weight after (sec) : 56.350697
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- subgradients found after (sec) : 0.201402
-, lowerbound = 15.000000 
+ finished changing the variable's weight after (sec) : 56.627250
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- new dual found after (sec) : 0.201415
+ finished changing the variable's weight after (sec) : 56.904353
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+
+ finished changing the variable's weight after (sec) : 57.184996
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+ finished changing the variable's weight after (sec) : 57.460993
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+ finished changing the variable's weight after (sec) : 57.755430
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+ finished changing the variable's weight after (sec) : 58.035246
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+ finished changing the variable's weight after (sec) : 58.316573
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+ finished changing the variable's weight after (sec) : 58.594071
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+ finished changing the variable's weight after (sec) : 58.879717
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+ finished changing the variable's weight after (sec) : 59.165843
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+ finished changing the variable's weight after (sec) : 59.444060
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+ finished changing the variable's weight after (sec) : 59.724004
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+ finished changing the variable's weight after (sec) : 60.003931
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+ finished changing the variable's weight after (sec) : 60.286439
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+ finished changing the variable's weight after (sec) : 60.565716
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+ finished changing the variable's weight after (sec) : 60.844620
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+ finished changing the variable's weight after (sec) : 61.122418
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+ finished changing the variable's weight after (sec) : 61.399423
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+ finished changing the variable's weight after (sec) : 61.676005
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+ finished changing the variable's weight after (sec) : 61.954265
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+ finished changing the variable's weight after (sec) : 62.231335
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+ finished changing the variable's weight after (sec) : 62.510231
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+ finished changing the variable's weight after (sec) : 62.796663
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+ finished changing the variable's weight after (sec) : 63.074390
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+ finished changing the variable's weight after (sec) : 63.344582
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+ finished changing the variable's weight after (sec) : 63.616327
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+ finished changing the variable's weight after (sec) : 64.158874
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+ finished changing the variable's weight after (sec) : 64.436492
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+ finished changing the variable's weight after (sec) : 64.996926
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+ finished changing the variable's weight after (sec) : 65.280591
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+ finished changing the variable's weight after (sec) : 65.840253
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+ finished changing the variable's weight after (sec) : 66.397988
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+ finished changing the variable's weight after (sec) : 66.961099
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+ finished changing the variable's weight after (sec) : 67.242815
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+ finished changing the variable's weight after (sec) : 67.523976
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+ finished changing the variable's weight after (sec) : 67.823484
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+ finished changing the variable's weight after (sec) : 69.141482
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+ finished changing the variable's weight after (sec) : 69.421000
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+ finished changing the variable's weight after (sec) : 69.980273
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diff --git a/var.txt b/var.txt
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..d628d834b7cb6a1ddc7e9cad90a9e0535ec5420e 100644
--- a/var.txt
+++ b/var.txt
@@ -0,0 +1,651 @@
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