diff --git a/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2 b/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2
index 640df855bbe42bbe113daebb918cc7c5e1f7589e..5e83e6ad70999dad48a0743cdcd6053bfa4fbf60 100755
Binary files a/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2 and b/bin/LagrangianRelaxation.linux.x86_64.gnu.opt.spx2 differ
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 cb83018ae4b8f24c70f87946b7c93b75bdc81d4e..de698773739367e637abb7408e08ff3ad7ec3c81 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/src/src/cmain.c b/src/src/cmain.c
new file mode 100644
index 0000000000000000000000000000000000000000..4bc344b5a4c2fa291459a8d63440cbc595f5f146
--- /dev/null
+++ b/src/src/cmain.c
@@ -0,0 +1,95 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  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   Relaxator/src/cmain.c
+ * @brief  Main file for C compilation
+ * @author Benjamin Mueller
+ */
+
+/*--+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+
+
+#include "scip/scip.h"
+#include "scip/scipdefplugins.h"
+#include "scip/scipshell.h"
+
+#include "relax_lagr.h"
+
+// #include "probdata_lagr.h"
+//#include "vardata_lagr.h"
+
+
+/** runs the shell */
+static
+SCIP_RETCODE runShell(
+   int                   argc,               /**< number of shell parameters */
+   char**                argv,               /**< array with shell parameters */
+   const char*           defaultsetname      /**< name of default settings file */
+   )
+{
+   SCIP* scip = NULL;
+
+   /*********
+    * Setup *
+    *********/
+
+   /* initialize SCIP */
+   SCIP_CALL( SCIPcreate(&scip) );
+
+   /* include plugins */
+   SCIP_CALL( SCIPincludeDefaultPlugins(scip) );
+   SCIP_CALL( SCIPincludeRelaxlagrangian(scip) );
+
+   /* we disable the presolve for the generation of the columns */
+   // SCIP_CALL( SCIPsetIntParam(scip,"presolving/maxrestarts",0) );
+   // SCIP_CALL( SCIPsetIntParam(scip,"presolving/maxrounds",0));
+
+
+   /**********************************
+    * Process command line arguments *
+    **********************************/
+
+   SCIP_CALL( SCIPprocessShellArguments(scip, argc, argv, defaultsetname) );
+
+   /********************
+    * Deinitialization *
+    ********************/
+
+   SCIP_CALL( SCIPfree(&scip) );
+
+   /* check block memory */
+   BMScheckEmptyMemory();
+
+   return SCIP_OKAY;
+}
+
+/** main method */
+int main(
+   int                   argc,               /**< number of shell parameters */
+   char**                argv                /**< array with shell parameters */
+   )
+{
+  SCIP_RETCODE retcode;
+
+  retcode = runShell(argc, argv, "scip.set");
+
+  if( retcode != SCIP_OKAY )
+  {
+     SCIPprintError(retcode);
+     return -1;
+  }
+
+  return 0;
+}
diff --git a/src/src/mL.ipynb b/src/src/mL.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..208337d8f1d36032b0451d9992a9605306135a19
--- /dev/null
+++ b/src/src/mL.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import datasets, transforms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": "<Figure size 1500x800 with 32 Axes>"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_size = 32\n",
+    "\n",
+    "transform_train = transforms.Compose([\n",
+    "    transforms.ToTensor()\n",
+    "])\n",
+    "\n",
+    "transform_test = transforms.Compose([\n",
+    "    transforms.ToTensor()\n",
+    "])\n",
+    "\n",
+    "\n",
+    "# datasets (MNIST)\n",
+    "mnist_train = datasets.MNIST('../data', train=True, download=True, transform=transform_train)\n",
+    "mnist_test = datasets.MNIST('../data', train=False, download=True, transform=transform_test)\n",
+    "\n",
+    "# dataloaders\n",
+    "train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, pin_memory=True)\n",
+    "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, pin_memory=True)\n",
+    "\n",
+    "\n",
+    "def visualize_batch(batch, labels, ncols=8):\n",
+    "    nrows = (batch.shape[0] + ncols - 1) // ncols\n",
+    "    plt.figure(figsize=(15, 2*nrows))\n",
+    "    for i in range(batch.shape[0]):\n",
+    "        plt.subplot(nrows, ncols, i+1)\n",
+    "        plt.imshow(batch[i].permute(1, 2, 0).squeeze(), interpolation='bilinear')\n",
+    "        plt.title(labels[i])\n",
+    "        plt.axis('off')\n",
+    "    plt.show()\n",
+    "batch, labels = next(iter(train_loader))\n",
+    "visualize_batch(batch, [str(int(lbl)) for lbl in labels]) \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.4 64-bit ('base': conda)",
+   "name": "python394jvsc74a57bd0cd73fa89a41ceeab93dd5b5f05d3f59878ac8a5687a0ed59509bcf085090ea10"
+  },
+  "language_info": {
+   "name": "python",
+   "version": ""
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/src/src/probdata_lagr.cpp b/src/src/probdata_lagr.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..32eab2dd2583f967608cff79af88e0331b523064
--- /dev/null
+++ b/src/src/probdata_lagr.cpp
@@ -0,0 +1,715 @@
+#include "probdata_lagr.h"
+#include "vardata_lagr.h"
+
+
+#include <iostream>
+#include <assert.h>
+#include <stdlib.h>
+#include <string.h>
+#include "relax_lagr.h"
+/*Using ProbData as a memory location for the constraints*/
+struct SCIP_ProbData
+{
+   SCIP_CONS**          SlotConss;
+   SCIP_CONS**          StartConss;
+   int*                 varids;                      /**<Ids for the variables found in the slot constraint*/
+   int                  nSlotConss;                   // number of slot constraints.
+   int                  nStartConss;
+   SCIP_Real*           upperbound;
+   int                  nbadconss;
+   int                  ngoodconss;
+   int*                 badconss;
+   int*                 goodconss;
+   int                  allnconsvars;
+   int*                 listnconsvars;
+   int*                 listconsvarids;
+
+};
+
+int* SCIPslotgetvarids(
+SCIP_ProbData* probdata
+){
+    return probdata->varids;
+ }
+
+int* SCIPlistnconsvars(
+SCIP_ProbData* probdata
+){
+    return probdata->listnconsvars;
+ }
+
+int* SCIPlistconsvarids(
+SCIP_ProbData* probdata
+){
+    return probdata->listconsvarids;
+ }
+
+SCIP_CONS** SCIPgetSlotConss(
+SCIP_ProbData* probdata
+){
+    return probdata->SlotConss;
+ }
+
+int SCIPgetNSlotConss(
+SCIP_ProbData* probdata
+){
+    return probdata->nSlotConss;
+ }
+
+int SCIPgetallnconsvars(
+SCIP_ProbData* probdata
+){
+       return probdata->allnconsvars;
+   }
+
+SCIP_RETCODE GetNGoodandNbad(
+   SCIP* scip,
+   int* nbad,
+   int* ngood,
+   SCIP_PROBDATA** probdata
+
+){
+   // int nbad=0;
+   // int ngood=0;
+   SCIP_Bool success;
+   SCIP_CONS** conss = SCIPgetConss(scip);
+   for(int r=0; r<SCIPgetNConss(scip); r++)
+   {
+      SCIP_CONS* cons = conss[r];
+      
+      //Now we add the condition(criteria) for separating the good and bad constraints. Let's first try inequality as a bad constraint
+      //first we get the number of good and bad constraints.
+
+      if(SCIPconsGetLhs(scip,cons,&success)==-SCIPinfinity(scip))                        /*<We get the slot constraints based on the inquality*/
+      {     
+         ++(*nbad);
+      }
+
+      else
+      {
+         ++(*ngood);
+      }
+   } 
+   printf("\n%d, %d, %d\n",*nbad, *ngood, SCIPgetNConss(scip));
+   assert(nbad+ngood=SCIPgetNConss(scip));
+   // SCIP_CALL(SCIPallocMemory(scip,probdata));
+   // (*probdata)->nbad = *nbad;
+   // (*probdata)->ngood = *ngood;
+   //* we now store the ids of the constraints classified as good or bad. 
+
+
+   return SCIP_OKAY;
+}
+
+
+/* here we create the probdata, which will be called for storage of values to the data*/
+SCIP_RETCODE probdataCreate(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_PROBDATA**       probdata,           /**< pointer to problem data */
+   SCIP_CONS**          SlotConss,
+   // SCIP_CONS**          StartConss,
+   int                   nSlotConss,
+   int                   nStartConss   
+   )       /**< number of slot constraints */
+{
+   assert(scip != NULL);
+   assert(probdata != NULL);
+   assert(SlotConss!=NULL);
+   assert(nStartConss >= 0);
+   assert(nSlotConss >= 0);
+
+   /* allocate memory */
+   SCIP_CALL( SCIPallocBlockMemory(scip, probdata) );
+   // BMSclearMemory(*probdata);
+
+   SCIP_CALL( SCIPallocBlockMemoryArray(scip, &(*probdata)->SlotConss, nSlotConss) );
+   // BMSclearMemoryArray((*probdata)->SlotConss, nSlotConss);
+   /* duplicate memory*/
+   SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(*probdata)->SlotConss, SlotConss, nSlotConss));           
+   (*probdata)->nSlotConss = nSlotConss;
+   (*probdata)->nStartConss = nStartConss;
+
+   return SCIP_OKAY;
+}
+
+/* here we FREE the probdata*/
+SCIP_RETCODE probdataFree(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_PROBDATA**       probdata           /**< pointer to problem data */
+   )
+{
+   assert(scip != NULL);
+   assert(probdata != NULL);
+   assert(*probdata!=NULL);
+
+   /* free memory */
+   if((*probdata)->SlotConss != NULL)
+   {
+      SCIPfreeBlockMemoryArray(scip, &(*probdata)->SlotConss, (*probdata)->nSlotConss);
+   }
+   if((*probdata)->StartConss != NULL)
+   {
+      SCIPfreeBlockMemoryArray(scip, &(*probdata)->StartConss, (*probdata)->nStartConss);
+   }
+   SCIPfreeBlockMemory(scip, probdata);
+   
+   return SCIP_OKAY;
+}
+
+
+/*We will create the probdata to have the slot constraints and the variables they hold withn them*/
+
+SCIP_RETCODE SCIPcreateprobdata
+(   SCIP*                  relaxscip,
+    SCIP_ProbData**        probdata,
+    SCIP_CONS**            conss,
+    SCIP_VAR**             vars,
+    SCIP_VAR***            varbuffers,  
+    int**                   badconss
+)
+{
+   int nconss = SCIPgetNConss(relaxscip);
+   int nvars = SCIPgetNVars(relaxscip);
+   int nSlotConss = 0;
+   int id =0;
+   SCIP_Bool success;
+   for (int t=0; t<nconss; t++)                             /* (3) */
+   {
+      SCIP_CONS* cons = conss[t];
+      if(SCIPconsGetLhs(relaxscip,cons,&success)==-SCIPinfinity(relaxscip))                        /*<We get the slot constraints based on the inquality*/
+      {
+         ++nSlotConss;  
+      }
+   }
+   printf("%d nslot conss",nSlotConss);
+
+   int allnconsvars=0;
+   int nconsvars=0;
+   int counter = 0; 
+   int maxnconsvars = 0;
+
+
+   SCIP_CALL(SCIPallocBufferArray(relaxscip, badconss, nSlotConss));         //the badconss array will contain the row number of the bad conss  
+
+   /*
+      we first get the row numbers of the bad conss, and save to the array: badconss.
+   */
+   for(int r = 0; r<nconss; ++r)
+   {
+      SCIP_CONS* cons = conss[r];
+      
+      if(SCIPconsGetLhs(relaxscip,cons,&success)==-SCIPinfinity(relaxscip))                        /*<We get the slot constraints based on the inquality*/
+      {
+         (*badconss)[counter]=r;
+         SCIP_CALL(SCIPgetConsNVars(relaxscip,cons,&nconsvars,&success));
+         counter++;
+         if(maxnconsvars<nconsvars){maxnconsvars=nconsvars;}
+         allnconsvars+=nconsvars;
+      }
+   }
+
+   /*
+      Our first objective is to create an array, containing the non-zero variables 
+      found in each bad conss. But instead of listing them in multiple rows, we list them just in one. 
+      for example: slot1 have vars[0] and vars[10] non-zero and slot2 has vars[1] and vars[11]. 
+      listconsvarids = {0,10,1,11, ...}
+   */
+   int* listconsvarids;   //examples {0,10,1,11,2,12,...} we use the 
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&listconsvarids,allnconsvars));
+   
+   /*
+      we save the number of non-zero variables in bad constraint has.
+   */
+   int* listnconsvars;  //example {2,2,2,3,3,3}
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&listnconsvars,nSlotConss));
+   
+   /*to not allocate buffer array over and over again, we create one, with
+   size = maxnconsvars. and then we intialize it with the first few variables from vars. 
+   */
+   SCIP_CALL(SCIPallocBufferArray(relaxscip, varbuffers, maxnconsvars)); 
+   for (int v = 0; v < maxnconsvars; v++)
+   {
+      (*varbuffers)[v] =vars[v];
+   }
+
+   
+   counter=0;
+   for (int r = 0; r < nSlotConss; ++r)
+   {
+      nconsvars=0;
+      id = (*badconss)[r];
+      SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+
+      // printf("%s \t",SCIPconsGetName(cons));
+      SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &success)); 
+      SCIP_CALL(SCIPgetConsVars(relaxscip, cons, (*varbuffers), nconsvars, &success));
+      if (!success){
+      abort(); }
+      listnconsvars[r] = nconsvars;
+      for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+      {
+         SCIP_VAR* varx = (*varbuffers)[j];
+         int varbufindex = SCIPvarGetIndex(varx);
+         assert(varbufindex != NULL);
+         listconsvarids[counter]=varbufindex;
+         counter++;
+      }
+   }
+
+   // counter=0;
+   // for(int r=0; r<nSlotConss;++r)
+   // {
+   //    for(int s=counter;s<(counter+listnconsvars[r]);++s)
+   //    {
+   //       printf("%s->",SCIPvarGetName(vars[listconsvarids[s]]));
+   //    }
+   //    printf("\n");
+   //    counter += listnconsvars[r];
+   //    // printf("%d\n->",counter);
+   // }
+   int* listvarswconsid;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&listvarswconsid,allnconsvars));
+
+   //* use the following to create an alternative way of varids*//
+   // int* listvarncons;
+   // SCIP_CALL(SCIPallocBufferArray(relaxscip,&listvarncons,nvars));
+
+   // int count;
+   // for(int v=0; v<nvars;++v)
+   // {
+   //    int varid = SCIPvarGetIndex(vars[v]);
+   //    counter=0;
+   //    count =0;
+      
+   //    // printf("%s->",SCIPvarGetName(vars[v]));
+   //    for(int r=0; r<nSlotConss;++r)
+   //    {
+         
+   //       id = (*badconss)[r];
+   //       SCIP_CONS* cons = SCIPgetConss(relaxscip)[id]; 
+   //       // printf("%s \t",SCIPconsGetName(cons));
+   //       for(int s=counter;s<(counter+listnconsvars[r]);++s)
+   //       {
+   //          // printf("(%d)",s);
+   //          if(varid==listconsvarids[s])
+   //          {
+   //             listvarswconsid[s]=id;
+   //             count++;
+   //             // printf("%s->",SCIPconsGetName(conss[listvarswconsid[s]]));
+   //          }
+   //       }
+   //       counter += listnconsvars[r];
+   //       // printf("%d\n->",counter);
+   //    }
+   //    listvarncons[v]=count;
+   //    // printf("\n");
+   // }
+   
+
+
+   // counter=0;
+   // for(int v=0; v<nvars;++v)
+   // {
+   //    SCIP_VAR* var = vars[v];
+   //    printf("%s,%d",SCIPvarGetName(var),listvarncons[v]);
+   //    //next step is a creation of an array that will tell in how many bad constraints a variable is found in. 
+   //    for(int s=counter;s<(counter+listvarncons[v]);++s)
+   //    {
+   //       // printf("%s->",SCIPconsGetName(conss[listconsvarids[s]]));
+   //       printf("%s,",SCIPconsGetName(conss[listconsvarids[s]]));
+   //    }
+   //    printf("\n");
+   //    counter += listvarncons[v];
+   // //    // printf("%d\n->",counter);
+   // }
+
+
+
+   SCIP_CALL(SCIPallocMemory(relaxscip,probdata));
+   (*probdata)->nSlotConss = nSlotConss;
+   (*probdata)->allnconsvars= allnconsvars;  //sum of all nconsvars;
+   (*probdata)->listnconsvars = listnconsvars;
+   (*probdata)->listconsvarids = listconsvarids;
+   return SCIP_OKAY;
+}
+
+
+/*************************************************************************************************/
+/* This is a function that will take a constraint(slot), and computes the following formula      */
+//*     >>dualMultiplier(cons)=min {SCIPvarGetQuotient for each of the variable in the cons}  */
+/* the function returns this minimum value                                                       */
+/**************************************************************************************************/
+SCIP_Real SCIPconsGetMultiplier(SCIP* scip,SCIP_CONS** cons,SCIP_Real subgradient,SCIP_Real C, SCIP_Real stepsize,SCIP_Bool firstiteration, SCIP_Real dualval)
+{
+   
+   SCIP_Real min;
+   SCIP_Bool success;
+   if(SCIPconsGetLhs(scip,*cons,&success)==-SCIPinfinity(scip))
+   {
+      if(firstiteration==true)
+      {
+         min = SCIPinfinity(scip);                                   //*we take a very big number for a comparision that will happen later on(i ust this as I'm not sure how to assing the infinity value in SCIP)                         
+         SCIP_VAR** varbuffer;
+         
+         SCIP_VARDATA*  vardata;    
+
+         int nconsvars;
+         SCIP_CALL(SCIPgetConsNVars(scip,*cons,&nconsvars,&success));
+         assert(nconsvars!=0);
+         SCIP_CALL(SCIPallocBufferArray(scip, &varbuffer, nconsvars));
+         SCIP_CALL(SCIPgetConsVars(scip,*cons,varbuffer,nconsvars,&success));
+         if (!success)
+            abort();
+         if(nconsvars==0)
+         {
+            min = 0;                     //to make sure that if the constraint doesn't have any variables, the dual will be 0. 
+         }
+         else
+         {
+            SCIP_Real compare[nconsvars] = {0};
+            for(int j=0;j<nconsvars;j++)
+            {
+               SCIP_VAR* consvar= varbuffer[j];
+               vardata = SCIPvarGetData(consvar); 
+               compare[j]=SCIPvarGetQuotient(vardata);
+               
+               if (compare[j]<min)
+               {
+                  
+                  min = compare[j];
+
+               }
+            }
+            if (min<0){min = 0;}     
+         }    
+         return min;              
+      }
+
+      else
+      {
+         
+         min = dualval + subgradient*stepsize;
+         
+         if(min < 0)
+         {
+            //prinf("-ve min %f",min);
+            return 0;
+
+         }
+         else
+         {   
+            //prinf("min %f = (subgradients[r]->%f*stepsize->%f *C->%f\n) = ",min,subgradient,stepsize,C);
+            return min;
+         }
+      }
+   }
+}
+      
+
+
+
+/******************************************************************************************************************/
+/* the next step would be to maximize over the Lagrangian dual Z(dual). This would give us the first iteration     */
+//* subgradient^{0}_{r} = sum{x[v]}-1, where x[v] is the solution value of the variables founds in the r-th conss  */
+/* the abover formula gives us the 0'th iteration of the subgradiant vector                                        */
+/*******************************************************************************************************************/
+SCIP_Real SCIPgetSubgradients(
+   SCIP* relaxscip,
+   SCIP_CONS* cons,
+   SCIP_Real* solvals
+  
+){
+   SCIP_VARDATA* vardata;
+   SCIP_Real addsolval = -1;
+   SCIP_Bool success;
+   int nconsvars; 
+   SCIP_VAR** varbuffer;
+
+   // SCIP_SOL* bestsol = SCIPgetBestSol(relaxscip);
+
+   // SCIP_CALL(SCIPprintSol(relaxscip,bestsol,filos,FALSE));
+   SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &success));
+   if(nconsvars==0){return 0;}
+   else
+   {
+      SCIP_CALL(SCIPallocBufferArray(relaxscip, &varbuffer, nconsvars));             /* (7) */
+      /** collect constraint variables in array "varbuffer" */
+      SCIP_CALL(SCIPgetConsVars(relaxscip, cons, varbuffer, nconsvars, &success));
+      
+      
+      // printf("*he*\n");
+      for (int j = 0; j < nconsvars; j++)
+      {
+         SCIP_VAR* consvar = varbuffer[j];
+         vardata = SCIPvarGetData(consvar);
+         
+         const char * consvarname = SCIPvarGetName(consvar);
+         //prinf("\t\t (%s,%f)\n",consvarname, solvals[SCIPvardataGetVarID(vardata)]);
+         addsolval += solvals[SCIPvardataGetVarID(vardata)];   
+         
+      }
+   }
+
+
+   
+
+   return addsolval;
+}
+
+
+#define btoa(x) ((x)?"true":"false")
+void extract(char *s,char *t,char *d,int pos,int len)
+{
+
+   s=s+(pos-1);
+   t=s+len;
+   while(s!=t)
+   {
+   *d=*s;
+   s++;
+   d++;
+   }
+   *d='\0';
+
+
+}
+/*************************************************************************************************/
+/*Copies the scip instance and then delete its slot constraints.                                 */
+/*solve it afterwards and save the best solution under bestsol .                                 */
+/*load an array that holds the solution value to each variable. SCIP_Real* solvals               */
+/*if solution got better in the iteration, then we save all the sol and obj val in bestsolvals   */
+/*************************************************************************************************/
+SCIP_RETCODE SCIPsolveiteration(SCIP* scip,int nSlotConss,SCIP_Real** subgradients, SCIP_Real C, SCIP_Real* stepsize,SCIP_Real** bestsolvals,SCIP_Real* upperbound,int niter)
+{
+   SCIP*                                  lagrscip;
+   SCIP_HASHMAP*                          varmap;
+   SCIP_HASHMAP*                          consmap;
+   
+  
+   int nvars = SCIPgetNVars(scip);
+   // SCIP_Real C= 2;
+   
+   SCIP_CALL( SCIPcreate(&lagrscip));
+   SCIP_Bool valid=FALSE;
+   // SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(lagrscip), nvars) );
+   SCIP_CALL(SCIPcopy(scip,lagrscip,varmap,consmap,"lagrscip",FALSE,FALSE,FALSE,FALSE,&valid));
+   
+   /*delete slot conss*/
+   SCIP_CONS** conss = SCIPgetConss(lagrscip);
+   for (int r = 0 ; r<nSlotConss; ++r)
+   {
+      SCIP_CONS* cons = conss[r];
+
+      
+      SCIP_CALL(SCIPdelConsLocal(lagrscip,cons));
+   }
+   // SCIPprintOrigProblem(lagrscip,NULL,"lp",FALSE);
+   
+   /*solves the problem only with the start constraints*/ 
+   SCIPsolve(lagrscip);
+   
+   /*get the best solution*/
+   SCIP_SOL* bestsol ;
+   SCIP_Real* solvals;
+   SCIP_CALL(SCIPallocBufferArray(lagrscip,&solvals,nvars+1));
+
+   SCIP_Real* secondsolvals;
+   SCIP_CALL(SCIPallocBufferArray(lagrscip,&secondsolvals,nvars+1));
+   // SCIPallocCleanBuffer(lagrscip,&secondsolvals);
+   
+   bestsol = SCIPgetBestSol(lagrscip);
+   // SCIP_CALL(SCIPprintBestSol(lagrscip,NULL,TRUE));
+
+   /*store the solution in solvals so we can later export it to subgradient function*/
+   SCIPgetSolVals(lagrscip,SCIPgetSols(lagrscip)[0],nvars,SCIPgetVars(lagrscip),solvals);                                   
+   
+   
+   //prinf("\n");
+   for (int v = 0; v<nvars-1; ++v)
+   {
+      if( solvals[v] == 1 && solvals[v+1] == 1)
+      {
+         solvals[v] = 0;   
+      }
+      secondsolvals[v]=solvals[v];
+      //prinf("(%s,%f,%f)\n",SCIPvarGetName(var), secondsolvals[v],SCIPgetVarSol(lagrscip,var));
+     
+      
+   }
+
+   /*to get a better upperbound*/
+   // SCIPgetFeasUpperbound(scip,&secondsolvals,&upperbound, &solobj, niter);
+   
+   /*if the obj value in this iteration is bette(in our case, higher), than the previous one, save the solution array and also it's obj value to the bestsolvals*/
+
+   if(SCIPgetSolOrigObj(lagrscip,bestsol) > (*bestsolvals)[nvars])
+   {
+      for (int v = 0; v<nvars; ++v)
+      {
+         *(&(*bestsolvals)[v])=solvals[v];
+         (*bestsolvals)[v] = secondsolvals[v];
+      }
+      *(&(*bestsolvals)[nvars]) = SCIPgetSolOrigObj(lagrscip,bestsol);
+      // *stepsize = (*upperbound) - 5*SCIPgetSolOrigObj(lagrscip,bestsol);
+      
+   }
+   else
+   {
+      // *stepsize = -10; 
+   }
+   //prinf("\t \t \tobj of sol %f, best sol %f", SCIPgetSolOrigObj(lagrscip,bestsol), (*bestsolvals)[nvars]);
+   SCIP_Real sqsum = 0;
+
+   /*get the subgradient for each slot constraint. i.e. sum all the solution values of the variables in the slot, and subtract 1*/
+   for (int r = 0 ; r<nSlotConss; ++r)
+   {
+      SCIP_CONS* cons = SCIPgetConss(scip)[r];
+      (*subgradients)[r] = SCIPgetSubgradients(scip,cons,solvals);
+      sqsum+=(*subgradients)[r]*(*subgradients)[r];
+      // //prinf("subgrad %f",(*subgradients)[r]);
+      
+   }
+   
+   *stepsize = 2*(SCIPgetSolOrigObj(lagrscip,bestsol)-C)/sqrt(sqsum);
+
+   (*bestsolvals)[nvars+1+niter]=SCIPgetSolOrigObj(lagrscip,bestsol);
+   printf("\n upp %f \t,sqsum =%f->%f, \tstpz=%f, \tsolobj= %f, \tbestsol = %f)\n", (*upperbound),sqsum,sqrt(sqsum),*stepsize, (*bestsolvals)[nvars+1+niter], (*bestsolvals)[nvars]);
+
+   
+
+   // bestsol = {0};
+
+   
+   SCIP_CALL(SCIPfreeTransform(lagrscip));
+   return SCIP_OKAY;
+}
+              
+/*to get a feasible upperbound*/
+SCIP_RETCODE SCIPgetFeasUpperbound(SCIP* scip, SCIP_Real** solvals, SCIP_Real** upperbound, SCIP_Real* solobj, int niter)
+{
+   
+   if(niter == 0)
+   {
+      // SCIP_CALL(SCIPfreeTransform(scip));
+      // SCIP_PROBDATA* probdata;
+      int nvars = SCIPgetNVars(scip);
+      SCIP_VAR** vars = SCIPgetVars(scip);
+
+      // for (int v = 0; v<nvars; ++v)
+      // {
+      //    (*solobj) += (*solvals)[v]*SCIPvarGetObj(vars[v]);
+      //    printf("\t %f\n", (*solvals)[v]);
+      // }
+      // printf("UPP %f and solobj %f\n",*(*upperbound), (*solobj));
+      
+      
+      for (int v = 0; v<nvars; ++v)
+      {
+      
+         int Q = int(v/9);
+         printf("%d, %f\n", Q, (*solvals)[v]);
+
+         for (int j = 1; j<6-Q;++j)
+         {
+            printf("\t(%s,%f) with (%s,%f)\n",SCIPvarGetName(SCIPgetVars(scip)[v]),(*solvals)[v],SCIPvarGetName(SCIPgetVars(scip)[v+9*j]),(*solvals)[v+9*j]);
+            if ((*solvals)[v] ==1 &&(*solvals)[v]==(*solvals)[v+9*j])
+            {
+               
+               (*solvals)[v+9*j] = 0;
+               (*solvals)[v+9*j+1] = 1;
+               
+            }
+         }
+         
+      }
+      
+      *(*upperbound) = 0;
+      for (int v = 0; v<nvars; ++v)
+      {
+         printf("\t\t\t%f",(*solvals)[v]);
+         printf("\t(%s,%f)\n",SCIPvarGetName(SCIPgetVars(scip)[v]),(*solvals)[v]);
+         (*(*upperbound)) += (*solvals)[v]*SCIPvarGetObj(vars[v]);
+         // printf("\t (%f(*solvals)f)\n",SCIPvarGetObj(SCIPgetVars(scip)[v]),*upperbound);
+         
+      }
+      printf("\t***UPPP %f \n",*(*upperbound));
+      // probdata->upperbound = upperbound;
+   }
+   
+
+   return SCIP_OKAY;
+}
+
+SCIP_RETCODE scipgetsolutions(SCIP* scip, SCIP_VAR** vars, SCIP_Real** solvals, SCIP_Real* relaxval, SCIP_Real* dualmultipliers, SCIP_Real sumofduals,SCIP_SOL** bestsol)
+{
+   double sum;
+   FILE* varobj;
+   varobj=fopen("varobj.txt","wr");
+   FILE* problemstate;
+   problemstate = fopen("problemstate.txt","w+"); 
+   FILE* solutions;
+   solutions = fopen("solutions2.txt","w+");
+
+   SCIP_VARDATA* vardata;
+
+   int nvars = SCIPgetNVars(scip);
+   for(int v=0;v<nvars;v++)
+   {
+      SCIP_VAR* var = vars[v];
+      sum =SCIPvarGetObj(var);
+      
+      vardata=SCIPvarGetData(var);
+      int* varids = SCIPvardataGetvarids(vardata); 
+      int NVarInBadConss = SCIPvardataGetNVarInBadConss(vardata);
+
+      for(int t=0;t<NVarInBadConss;t++)
+      {
+         sum += dualmultipliers[varids[t]];
+         fprintf(varobj,"{%d, %f, %f\t",varids[t], dualmultipliers[varids[t]],sum);
+      }
+
+      
+      // findmin += sum*solvals[v];
+      // fprintf(varobj, "solval %f, coefficient %f, sum %f", solvals[v],sum, findmin);
+      fprintf(varobj,"}\n\n");
+      SCIP_CALL(SCIPaddVarObj(scip,var,sum));
+      // add = weights[v]+sum;
+      
+   }
+      
+   // SCIPinfoMessage(scip, TimeCollector, "\n finished changing the variable's weight after (sec) : %f\n", SCIPgetClockTime(scip, varslottime));
+   
+   SCIP_CALL(SCIPaddOrigObjoffset(scip,-1*sumofduals));
+   SCIP_CALL(SCIPprintOrigProblem(scip, problemstate, "lp", FALSE));
+   SCIPsetMessagehdlrQuiet(scip, TRUE);
+   // fclose(AfterPreProcessing);
+
+   SCIP_CALL( SCIPtransformProb(scip) );
+   SCIP_CALL( SCIPsolve(scip) );
+   *relaxval = SCIPgetPrimalbound(scip);
+   SCIPdebugMessage("relaxation bound = %e status = %d\n", *relaxval, SCIPgetStatus(scip));
+   /*get the best solution*/   
+   *bestsol = SCIPgetBestSol(scip) ;
+   SCIP_CALL(SCIPallocBufferArray(scip,solvals,nvars+1)); 
+
+   /*text output*/
+   fprintf(solutions,"first iteration \t bound=%f, \t objsol=%f \n",SCIPgetPrimalbound(scip),*relaxval);
+   SCIP_CALL(SCIPprintBestSol(scip,solutions,FALSE));
+
+   /*store the solution in solvals so we can later export it to subgradient function*/
+   SCIPgetSolVals(scip,*bestsol,nvars,vars,*solvals);
+   fclose(varobj);
+   fclose(solutions);
+   fclose(problemstate);
+}
+
+SCIP_Real getnorm(SCIP_Real* array, int sizeofarray, SCIP_Real stepsize)
+{
+   SCIP_Real norm;
+   for(int r=0; r<sizeofarray;++r)
+   {
+      norm += array[r]*array[r];
+   }
+   norm=sqrt(norm);
+   return norm;   
+}
\ No newline at end of file
diff --git a/src/src/probdata_lagr.h b/src/src/probdata_lagr.h
new file mode 100644
index 0000000000000000000000000000000000000000..de1c397f937960df4597e4e641191138c21fb5b0
--- /dev/null
+++ b/src/src/probdata_lagr.h
@@ -0,0 +1,104 @@
+/**@file   PROBDATA_lagr.h
+ * @brief  Problem data for Lagrangian relaxation
+ * @author Dawit Hailu
+ * 
+ *
+ * This file handles the main problem data used in the Lagrangian relaxation.
+ */
+/*---+----1----+----2----+----3----+----4----+----d5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+#ifndef __SCIP_PROBDATA_LAGR__
+#define __SCIP_PROBDATA_LAGR__
+
+#include "scip/scip.h"
+#include "vardata_lagr.h"
+
+
+/*classifying and storing the slot and start constraints in PROBDATA
+create probdata*/
+SCIP_RETCODE SCIPcreateprobdata(
+    SCIP*                  scip,
+    SCIP_ProbData**        probdata,
+    SCIP_CONS**            conss,
+    SCIP_VAR**            vars,
+    SCIP_VAR***            varbuffers,
+    int**                   badconss
+);
+
+int SCIPgetallnconsvars(
+SCIP_ProbData* probdata
+    );
+SCIP_RETCODE GetNGoodandNbad(
+   SCIP* scip,
+   int* nbad,
+   int* ngood,
+   SCIP_PROBDATA** probdata
+
+);
+
+/*get's the ids of the variables found in the slot constraints*/
+int* SCIPconsGetvarids(
+    SCIP_ProbData* probdata
+    );
+int SCIPgetallmaxnconsvars(
+SCIP_ProbData* probdata
+);
+
+int* SCIPlistnconsvars(
+SCIP_ProbData* probdata
+);
+
+SCIP_Real getnorm(SCIP_Real* array, int sizeofarray, SCIP_Real stepsize);
+
+int* SCIPlistconsvarids(
+SCIP_ProbData* probdata
+);
+
+SCIP_CONS** SCIPgetSlotConss(
+SCIP_ProbData* probdata
+    );
+SCIP_RETCODE scipgetsolutions(
+    SCIP* scip, SCIP_VAR** vars,
+    SCIP_Real** solvals, 
+    SCIP_Real* relaxval, 
+    SCIP_Real* dualmultipliers, 
+    SCIP_Real sumofduals,
+    SCIP_SOL** bestsol);
+
+int SCIPgetNSlotConss(
+SCIP_ProbData* probdata
+    );
+
+/* here we create the probdata, which will be called for storage of values to the data*/
+SCIP_RETCODE probdataCreate(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_PROBDATA**       probdata,           /**< pointer to problem data */
+   SCIP_CONS**          SlotConss,
+//    SCIP_CONS**          StartConss,
+   //SCIP_VAR**            vars,               /**< all exist variables */
+   //SCIP_CONS**           conss,              /**< set partitioning constraints for each job exactly one */
+   //int*                  varids,             /**< array of ids of variables in the slot constraints */
+   //int                   nconss,              /**< number of constraints */
+   int                   nSlotConss,          /**< number of slot constraints */
+int                      nStartConss
+   );
+
+
+SCIP_RETCODE probdataFree(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_PROBDATA**       probdata
+   );
+
+SCIP_Real SCIPconsGetMultiplier(SCIP* scip,SCIP_CONS** cons,SCIP_Real subgradient,SCIP_Real C, SCIP_Real stepsize,SCIP_Bool firstiteration, SCIP_Real dualval);
+
+SCIP_Real SCIPgetSubgradients(
+   SCIP* relaxscip,
+   SCIP_CONS* cons,
+   SCIP_Real* solvals
+);
+
+SCIP_RETCODE SCIPclassifyGoodBad(SCIP* scip, SCIP_CONS** conss, int nconss, SCIP_ProbData** probdata);
+
+SCIP_RETCODE SCIPsolveiteration(SCIP* scip,int nSlotConss,SCIP_Real** subgradients, SCIP_Real C, SCIP_Real* stepsize,SCIP_Real** bestsolvals,SCIP_Real* upperbound,int niter);
+
+SCIP_RETCODE SCIPgetFeasUpperbound(SCIP* scip,SCIP_Real** solvals, SCIP_Real** upperbound, SCIP_Real* solobj, int niter);
+#endif
diff --git a/src/src/relax.cpp b/src/src/relax.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..5859799433ee1ca9a55df63d2c25236198024a10
--- /dev/null
+++ b/src/src/relax.cpp
@@ -0,0 +1,863 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  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 <time.h>
+#include <chrono>
+#include <iostream>
+#include "relax_lagr.h"
+#include "scip/scipdefplugins.h"
+#include "scip/scip.h"
+#include "scip/cons_setppc.h"
+#include "scip/scip_mem.h"
+#include "scip/scip_prob.h"
+//#include "../../examples/Binpacking/src/vardata_binpacking.h"
+//#include "scip/struct_cons.h"
+
+#include "scip/def.h"
+
+
+
+#define RELAX_NAME             "lagr"
+#define RELAX_DESC             "relaxator template"
+#define RELAX_PRIORITY         500
+#define RELAX_FREQ             1
+
+
+
+
+/*
+ * Data structures
+ */
+
+/* TODO: fill in the necessary relaxator data */
+
+/** relaxator data */
+struct SCIP_RelaxData
+
+{
+   SCIP_SOL* sol;         /**current solution(working solution)*/
+};
+
+/*Using ProbData as a memory location for the constraints*/
+struct SCIP_ProbData
+{
+   SCIP_VAR**           vars;
+   int                  nvars;
+   SCIP_CONS**          SlotConss;                    //array with all slot constraits. i.e. those starting with 'c' or 'C' or 's' (slot.) 
+   int                  nSlotConss;                   // number of slot constraints.
+   SCIP_CONS**          StartConss;                   // array with all start constraits. i.e. those starting with 'F' or 'f'.
+   int                  nStartConss;                  // number of start constraints. 
+   SCIP_Real*           dualmultipliers;                          //lambda related with the slot constraint
+};
+
+static
+SCIP_CONS ** SCIPconsGetSlotConss(
+SCIP_ProbData* probdata
+){
+    return probdata->SlotConss;
+ }
+
+static
+SCIP_CONS ** SCIPconsGetStartConss(
+SCIP_ProbData* probdata
+){
+    return probdata->StartConss;
+ }
+
+static
+int SCIPconsGetnSlotConss(
+SCIP_ProbData* probdata
+){
+    return probdata->nSlotConss;
+ }
+
+static
+int SCIPconsGetnStartConss(
+SCIP_ProbData* probdata
+){
+    return probdata->nStartConss;
+ }
+
+static
+SCIP_Real* SCIPconsGetDualSlotconss(
+SCIP_ProbData* probdata
+){
+   return probdata->dualmultipliers;
+}
+
+struct SCIP_VarData
+{
+   
+	SCIP_CONS**          VarConss;                     /** array with all constraints where the var is occuring */
+	int                  nVarConss;                    /** number of constraints in VarConss */
+	SCIP_CONS**          VarSlotConss;                 /** array only with the slotconstraints where the var is occuring */
+	int                  nVarSlotConss;                /** number of slotconstraints in (slot)VarConss */   
+	SCIP_CONS*           VarStartConss;                /** VarStartConss where the var is occuring */
+	int                  nVarStartConss;               /** number of VarStartConsss in VarConss  (must be = 1)*/
+   //SCIP_Real            relaxobj;                     /**stores the relax object of the new scip*/
+   SCIP_Real*           subgradients;
+
+};
+
+static
+SCIP_CONS** SCIPvardataGetConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+{
+	return vardata->VarConss;
+}
+
+/** get number of constraints */
+static
+int SCIPvardataGetnVarConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+   
+)
+{
+	return vardata->nVarConss;
+}
+static
+int SCIPvardataGetnVarSlotConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+{
+	return vardata->nVarSlotConss;
+}
+/** End: parked methods */
+// static
+// SCIP_Real SCIPvardataGetrelaxobj(
+// 	SCIP_VARDATA* vardata     /**< variable data */
+// )
+// {
+// 	return vardata->relaxobj;
+// }
+
+
+/*
+ * Local methods
+ */
+
+/* put your local methods here, and declare them static */
+
+/*
+ * Callback methods of relaxator
+ */
+
+/* TODO: Implement all necessary relaxator methods. The methods with an #if 0 ... #else #define ... are optional */
+
+/** copy method for relaxator plugins (called when SCIP copies plugins) */
+#if 0
+static
+SCIP_DECL_RELAXCOPY(relaxCopylagr)
+{  /*lint --e{715}*/
+   SCIPerrorMessage("method of lagr relaxator not implemented yet\n");
+   SCIPABORT(); /*lint --e{527}*/
+
+   return SCIP_OKAY;
+}
+#else
+#define relaxCopylagr NULL
+#endif
+
+/** destructor of relaxator to free user data (called when SCIP is exiting) */
+
+SCIP_DECL_RELAXFREE(relaxFreelagr)
+{  /*lint --e{715}*/
+   //SCIPerrorMessage("start executing lagr\n");
+   SCIP_RELAXDATA* relaxdata;
+   
+   SCIPfreeBlockMemory(scip, &relaxdata);
+
+   
+   
+   return SCIP_OKAY;
+}
+
+
+/** initialization method of relaxator (called after problem was transformed) */
+
+static
+SCIP_DECL_RELAXINIT(relaxInitlagr)
+{  /*lint --e{715}*/
+    
+
+   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}*/
+   SCIPerrorMessage("method of lagr relaxator not implemented yet\n");
+   SCIPABORT(); /*lint --e{527}*/
+
+   return SCIP_OKAY;
+}
+#else
+#define relaxExitsollagr NULL
+#endif
+
+
+/** execution method of relaxator */
+static
+SCIP_DECL_RELAXEXEC(relaxExeclagr)
+{  /*lint --e{715}*/
+   //SCIP_ProbData* probdata;
+   SCIP* relaxscip;
+   SCIP_HASHMAP* varmap;
+   SCIP_HASHMAP* consmap;
+
+   SCIP_VAR** vars = SCIPgetVars(scip);
+   SCIP_CONS** conss = SCIPgetConss(scip);
+   SCIP_VAR** varbuffer;
+   SCIP_Bool success;
+   SCIP_Bool boundconstraint;
+   
+   
+   
+   
+   SCIP_RELAXDATA* relaxdata;
+   SCIP_ProbData* probdata;
+   SCIP_VARDATA* vardata;
+   
+   SCIP_CONS** VarConss;
+   SCIP_CONS** SlotConss;
+   SCIP_CONS** StartConss;
+   
+   SCIP_Real relaxobj;
+   SCIP_Real lambda = 0;
+   SCIP_Real * dualsolval;
+
+   int nVarConss = 0;
+   int nStartConss =0;
+   int nSlotConss = 0;
+
+   int nvars = SCIPgetNVars(scip);
+   int nconss = SCIPgetNConss(scip);
+
+   int t=0;
+   using std::cout;
+   FILE * file;
+   //SCIP_CALL(SCIPprintOrigProblem(scip, file, "lp", FALSE ));
+
+   for (t=0; t<nconss; t++)
+      {
+         
+         SCIP_CONS* cons = conss[t];
+         const char * consname = SCIPconsGetName(conss[t]);
+         char firstchar=*consname;
+         
+         if(firstchar == 'c'||firstchar=='s') 
+         {  
+            nSlotConss++;
+         }       
+         
+         else if (firstchar == 'F'||firstchar=='f')
+         {
+            nStartConss++;
+         }
+      
+      }
+   assert(nStartConss+nSlotConss==nconss);      //making sure we get the right number.
+
+
+   /*allocated a new memory for SlotConstraints, StartConstraints and their size*/
+   SCIP_CALL(SCIPallocBufferArray(scip, &SlotConss, nSlotConss));
+   SCIP_CALL(SCIPallocBufferArray(scip, &StartConss, nStartConss));
+   
+   nStartConss =0;
+   nSlotConss = 0;
+   for (t=0; t<nconss; t++)
+      {
+         
+         SCIP_CONS* cons = conss[t];
+         const char * consname = SCIPconsGetName(conss[t]);
+         //std::cout<<"\n"<<consname[0];
+         char firstchar=*consname;
+         //std::cout<<"\n"<<firstchar;
+         
+         if (firstchar == 'c'||firstchar =='s')
+         {  
+            SlotConss[nSlotConss]=cons;
+            nSlotConss++;
+            
+         }       
+         else if (firstchar == 'F'||firstchar=='f')
+         {
+            StartConss[nStartConss]=cons;
+            nStartConss++;
+         }
+      
+      }
+   cout<<"nslot"<<nSlotConss<<"\n";
+   cout<<"nstart"<<nStartConss<<"\n";
+   SCIP_CALL(SCIPallocBlockMemory(scip, &probdata));
+   SCIP_CALL(SCIPallocBlockMemoryArray(scip, &(probdata->SlotConss), nSlotConss));
+   SCIP_CALL(SCIPallocBlockMemoryArray(scip, &(probdata->StartConss), nStartConss));
+   SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(probdata->SlotConss), SlotConss, nSlotConss));
+   SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(probdata->StartConss), StartConss, nStartConss));
+   probdata->nStartConss = nStartConss;           //save this to the probdata
+   probdata->nSlotConss = nSlotConss;
+ 
+   // int x = SCIPconsGetnSlotConss(probdata);
+   // printf("\n  x= nconss = %d",SCIPconsGetnSlotConss(probdata));
+
+/** 
+   //"afterwards taken and adapted from Christopher's heurdata.c"
+ 
+/** 
+ * For the next code, we want to find the constraints that are connected with a certain variable, for example x_1_1. We create three for loops, first one is for the variables, for eg. x_1_1.
+ * after that we start a second loop for the constraints, let's say for C1, and we go through all the variables connected with this cons. Third loop will go through the variable in step 2 
+ * one by one and then compares it with the original var, x_1_1. If true(the indexes are the same), the constrait will be saved in the array designated, constraintarray or better name, varconss. 
+*/
+
+/** 
+ * The second part of the loop will be to separate the constraints attached with our variable, for example, c1,c2,F1. For this we create a for loop with size equaling to 3(for the ex.)
+ * we create an if condition that checkes if the cons starts with c or F. If c, then we create and allocate it to an array called "slotconstraintarray", else, "VarStartConssarray".
+ * We assure that one variable is only found in one of the start constraints with an assert function. 
+ * All of these will be saved under the vardata!
+ * 
+*/ 
+    //const auto t_start = std::chrono::system_clock::now();
+    auto start = std::chrono::system_clock::now();
+   //time_t begin = time(NULL);
+   //SCIP_CALL(SCIPallocBufferArray(scip, &var, nvars ));
+   int i;
+   for ( i = 0; i < nvars; i++)
+   {
+      
+      SCIP_VAR* var = vars[i]; //prints just locations
+      int varindex = SCIPvarGetIndex(var); //prints 51,89,90, 91, ..101
+      assert(varindex!= NULL);
+      
+      SCIP_CALL(SCIPallocBufferArray(scip, &VarConss, nconss ));
+    
+      int c;
+      for (c = 0; c < nconss; c++)
+      {
+         
+         SCIP_CONS* cons = conss[c];
+         int nconsvars;
+         /** request number of variables of constraint [c] */
+			SCIP_CALL(SCIPgetConsNVars(scip, cons, &nconsvars, &success));
+         if (!success)
+				abort();
+         /** allocate memory for the varbuffer arrays of the size of "nconsvars" */
+			SCIP_CALL(SCIPallocBufferArray(scip, &varbuffer, nconsvars));
+         /** collect constraint variables in array "varbuffer" */
+			SCIP_CALL(SCIPgetConsVars(scip, cons, varbuffer, nconsvars, &success));
+      	/** If no success, abort process */
+			if (!success)
+				abort();
+         int v;   
+			/** loop over constraint variables and compare varindices */
+			for (v = 0; v < nconsvars; ++v)
+			{
+      		SCIP_VAR* varx = varbuffer[v];
+      		int varindexx = SCIPvarGetIndex(varx);
+            assert(varindexx != NULL);
+
+				/** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+				if (varindex == varindexx) {
+					
+					VarConss[nVarConss] = cons;
+					nVarConss++;
+				}
+				else {
+					//printf("varindex != varindexx\n");
+				}
+            
+			}
+                  
+      }
+      /**Begin: copy constraintdata in vardata */
+
+      SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     /** allocate memory for vardata*/
+      /**allocate memory for VarConss in (struct) vardata */
+      SCIP_CALL(SCIPallocBlockMemoryArray(scip, &(vardata->VarConss), nVarConss));
+      /** copy array "constraintsarry" to vardata */
+      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->VarConss), VarConss, nVarConss));
+
+
+      vardata->nVarConss = nVarConss;  /**copy nVarConss to VarData */
+      
+
+      /**End: copy constraintdata in vardata */
+
+      /**Begin: create VarStartConss and slotconstraintarray and copy them to vardata */
+      SCIP_CONS* VarStartConss = NULL;
+      SCIP_CONS** VarSlotConss;
+
+      /**allocate memory for the VarSlotConss of the size of "nconss"*/
+      SCIP_CALL(SCIPallocBufferArray(scip, &VarSlotConss, nconss));
+    
+      int nVarSlotConss = 0;
+      int nVarStartConss = 0;
+
+      int e;
+      for (e = 0; e<nVarConss; ++e)
+      {
+
+         SCIP_CONS* cons = VarConss[e];
+         assert(cons != NULL);
+         char *ptr = SCIPconsGetName(cons);
+         //printf("[var %i %s, cons %i %s] \n", i,varname, c, ptr); 
+         char firstchar = *ptr;
+         //printf("%c ," , firstchar);
+
+         if(firstchar == 'c'||firstchar=='s') 
+         {
+            VarSlotConss[nVarSlotConss] = cons;
+            nVarSlotConss++;
+            
+         }
+         else if (firstchar == 'F'||firstchar=='f')
+         {
+            VarStartConss = cons;
+            nVarStartConss++;
+         }
+         else
+         {
+            printf("Error format constraint");
+            assert(0);  /**abort if this happens*/
+         
+         }
+         // int f;
+         // for (f = 0; f < nVarSlotConss; f++)
+         // {
+         //    printf("var %s, in cons %s, weight %f \n", SCIPvarGetName(vars[i]), SCIPconsGetName(VarSlotConss[f]), SCIPvarGetObj(vars[i]));
+         // }
+         
+      }
+      
+      
+      /** allocate memory for VarSlotConss in vardata */
+      SCIP_CALL(SCIPallocBlockMemoryArray(scip, &(vardata->VarSlotConss), nVarSlotConss));
+
+      /** copy array "VarSlotConss" to vardata */
+      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->VarSlotConss), VarSlotConss, nVarSlotConss));
+      
+
+      /**there must be exactly one VarStartConss*/
+      assert(nVarStartConss == 1);
+
+      /**copy "VarStartConss" to vardata */
+      vardata->VarStartConss = VarStartConss;
+
+      vardata->nVarStartConss = nVarStartConss;
+      vardata->nVarSlotConss = nVarSlotConss;
+
+
+
+      
+      
+      //cout<<"\n nvarconss is ="<<SCIPvardataGetnVarConss(vardata);
+    //  assert(vardata != NULL);
+      
+      //nVarSlotConss =  SCIPvardataGetnVarSlotConss(vardata);
+    
+      /*Get the relaxed obj*/
+      //vardata = SCIPvarGetData(var);
+      // lambda = 0.1;
+      // relaxobj = SCIPvarGetObj(var) + nVarSlotConss * lambda  ;          //cout<<"relaxobj for var\n"<<relaxobj; gives   -ve answers, please check later
+      // //cout<<"\n relaxobj for var"<<relaxobj; 
+      // relaxobj += nVarSlotConss * lambda;
+
+      // SCIP_CALL(SCIPduplicateMemory(scip,&(vardata->relaxobj), relaxobj));
+      // vardata->relaxobj =relaxobj;     
+      // cout<<"\n var"<<SCIPvarGetName(var)<<"       with no of constraints "<<SCIPvardataGetnVarSlotConss(vardata)<<"        old obj value"<<SCIPvarGetObj(var)<<"       new relaxobj "<<relaxobj;
+     
+      // cout<<"\n obj + new lambda * nvarslotconss =" <<relaxobj<<"\n";
+
+
+      SCIPvarSetData(var,vardata); 
+   
+      /**End: create VarStartConssarray and slot constraintarray and copy them to vardata */
+
+      /** free arrays */
+      SCIPfreeBufferArray(scip, &VarConss);
+      SCIPfreeBufferArray(scip, &VarSlotConss);
+      SCIPfreeBufferArray(scip,varbuffer);
+
+      /**reset variablecounters */
+      nVarConss = 0;
+      nVarStartConss = 0;
+      nVarSlotConss = 0;
+      relaxobj = 0;
+      
+      
+
+      
+
+      /** set the vardata for the variable in the loop */ 
+     
+   }
+   auto end = std::chrono::system_clock::now();
+
+  
+   printf("Time taken for the process of identifying the constraint for each variable is %5f seconds",end-start );
+
+   SCIP_CALL( SCIPcreate(&relaxscip));
+   //SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)));
+   
+   success = FALSE;
+   
+   SCIP_CALL( SCIPcopy(scip, relaxscip, NULL, consmap, "relaxscip", FALSE, FALSE, FALSE, FALSE, &success ));
+   //cout<<"just getting the name of the relaxvar"<<SCIPvarGetName(SCIPgetVars(relaxscip)[1]);
+
+   SCIP_VAR** lagrvars =SCIPgetVars(relaxscip);
+   SCIP_CONS** lagrconss = SCIPgetConss(relaxscip);
+
+//   lambda = 50;
+   
+
+   /* below, we have created the dual multipliers as an array and not just a fixed number. We will use the formula of the 0-th iteration(first one) of the r-th lambda(corrosponding to the slot conss) as:
+   lambda(^0)(_r)=min(j \in J) of  { (obj of the j-th   variable)/(the number of non-zero variables in the j-th column)  } 
+   
+   The way we do it will be, along each slot constraint, we would be comparing the minimum value (min) among a list of quotients(compare[j]). 
+   For the quotient, we need the consvars(variables found in the slot and thier objective value), and the non-zero vaiables in the column the consvars are located. 
+
+   compare[j]=consvarobj/SCIPvardataGetnVarSlotConss(vardata);
+
+   we intialize min with a big number and compare it with the j-th compare value. 
+
+   */ 
+
+      
+   SCIP_Real dualmultipliers[SCIPconsGetnSlotConss(probdata)];     //the size of the dualmultipliers array is same as the no of slot constraints. 
+   SCIP_Real sumofduals=0;                                         //will be used to save the sum of the dual multipliers
+   for ( int r = 0; r < SCIPconsGetnSlotConss(probdata); r++)
+   {
+      // SCIP_Real min=1000000000000;                                   //we take a very big number for a comparision that will happen later on                         
+      //cout<<"Cnconss"<<SCIPconsGetnSlotConss(probdata)<<"\n";
+      SCIP_CONS* cons = conss[r];
+      
+      
+      int nconsvars;
+      SCIP_CALL(SCIPgetConsNVars(scip,cons,&nconsvars,&success));
+      SCIP_CALL(SCIPgetConsVars(scip,cons,varbuffer,nconsvars,&success));
+
+      SCIP_Real compare[nconsvars];
+
+      for(int j=0;j<nconsvars;j++)
+      {
+         SCIP_VAR* consvar= varbuffer[j];
+         vardata = SCIPvarGetData(consvar);
+         SCIP_Real consvarobj=SCIPvarGetObj(consvar);
+         compare[j]=consvarobj/SCIPvardataGetnVarSlotConss(vardata);
+         //cout<<"the j-th dual of the dualmultipliers["<<j<<"] is"<<compare[j]<<"\n";
+         if (compare[j]<min)
+         {
+            
+            min = compare[j];
+         }
+
+      }
+      
+         
+      dualmultipliers[r]=min;
+      sumofduals-=min;
+      //cout<<"the dual multiplier is "<<dualmultipliers[r]<<"\n";
+      
+   } 
+   
+   /*
+   
+   In the next step, we reformulate the problem by adding the dual multipliers to the objective value of the variable. 
+   In order to do that, we use the nVarSlotConss to loop over the variable, so we can add the dualmultipliers associated with this variable. 
+   We intialize dualsum =0 and then add the dualmultipliers in the loop. 
+   we then make it part of the reformulation by using the funtion, SCIP_CALL(SCIPaddVarObj(relaxscip,lagrvar,dualsum));
+
+   example would be like min (obj[1]+dualsum)*x[1] where dualsum=sum(dualmulpliers[r]) where r=[0,nslotconst]. 
+
+   */
+
+   for (int v=0; v<nvars; v++)
+   {
+      SCIP_VAR* var = vars[v];
+      SCIP_VAR* lagrvar = lagrvars[v];
+      vardata = SCIPvarGetData(var);
+      SCIP_Real relaxobj = SCIPvarGetObj(var);
+      SCIP_Real dualsum = 0;
+      int nVarSlotConss = SCIPvardataGetnVarSlotConss(vardata);
+
+      for ( int j = 0; j < nVarSlotConss; j++)
+      {
+         SCIP_CONS** VarSlotConss =vardata->VarSlotConss;
+         SCIP_CONS* varslotcons = VarSlotConss[j];
+          
+         const char * varslotconsname = SCIPconsGetName(varslotcons);
+         
+         int nSlotConss = SCIPconsGetnSlotConss(probdata);
+         SCIP_CONS** SlotConss = SCIPconsGetSlotConss(probdata);
+
+         for(int r=0; r<nSlotConss; r++)
+         {
+            SCIP_CONS* cons = conss[r];
+            const char* consname = SCIPconsGetName(cons);
+            if(strcmp(consname,varslotconsname)==0)
+            {
+               dualsum+=dualmultipliers[r];
+               //cout<<" dualsum "<<dualsum;
+            }
+            else
+            {
+               continue;
+            }
+         }
+      }
+      //relaxobj += sum;
+      //cout<<" sum "<<dualsum<<" and new obj is "<<relaxobj+dualsum<<"\n";
+
+      SCIP_CALL(SCIPaddVarObj(relaxscip,lagrvar,dualsum));
+      //cout<<"\n var"<<SCIPvarGetName(var)<<"       found in "<<SCIPvardataGetnVarSlotConss(vardata)<<" constraints"<<"       old obj "<<SCIPvarGetObj(var)<<"           new relaxobj is "<<relaxobj+sum;
+
+   }
+   //SCIPprintTransProblem(relaxscip,file,"lp",FALSE);
+   // SCIP_Real slotlambda;
+
+
+
+   // for ( i = 0; i < SCIPgetNVars(scip) ; i++)
+   // {     
+   
+   //    SCIP_VAR* var =vars[i];
+   //    SCIP_VAR* lagrvar = lagrvars[i];
+      
+
+   //    vardata = SCIPvarGetData(var);
+   //    const char * name = SCIPvarGetName(var);
+
+
+   //    relaxobj = SCIPvarGetObj(var);          //cout<<"relaxobj for var\n"<<relaxobj; gives   -ve answers, please check later
+
+           
+      
+      
+   //    slotlambda = SCIPvardataGetnVarSlotConss(vardata) * lambda;
+   //    relaxobj += slotlambda;
+
+   //    SCIP_CALL(SCIPaddVarObj(relaxscip, lagrvar, slotlambda));
+   //    cout<<"lambda="<<lambda;
+   //    cout<<"\n var"<<SCIPvarGetName(var)<<"       found in "<<SCIPvardataGetnVarSlotConss(vardata)<<" constraints"<<"       old obj "<<SCIPvarGetObj(var)<<"           new relaxobj is "<<relaxobj;
+
+
+     
+
+   // }
+   
+    
+   /*delete all the slot constraints*/
+   for(i = 0; i<SCIPconsGetnSlotConss(probdata); ++i)                     
+   { 
+      SCIP_CONS* cons = SCIPgetConss(relaxscip)[i];
+      SCIP_CALL(SCIPdelCons(relaxscip,cons));                                              
+      
+   }
+   assert(SCIPgetNConss(relaxscip)!=0);                     //cout<<"\n nconss for the relaxscip"<<SCIPgetNConss(relaxscip)<<"\n";
+   
+  
+   //SCIP_Real negativesumoflambdas = - 1 * SCIPconsGetnSlotConss(probdata)*lambda;
+   SCIP_SOL** sols;
+   SCIP_SOL* bestsol;
+   int nsols;
+   int s;
+   SCIP_Real* vals;
+   
+   SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));        //we have added the negative of the sum of the dual multipliers
+   SCIP_CALL( SCIPtransformProb(relaxscip));                      //transform the problem.
+   SCIP_CALL(SCIPsolve(relaxscip));                                  //solves the problem. 
+   sols = SCIPgetSols(relaxscip);                                    //get the solutions.
+   nsols = SCIPgetNSols(relaxscip);                                  //gets the number of solutions
+   bestsol = SCIPgetBestSol(relaxscip);
+   // SCIP_CALL(SCIPprintBestSol(scip,file,FALSE));
+   //FILE * file;
+   const char * relaxlagr;
+   
+   SCIP_CALL(SCIPprintOrigProblem(relaxscip, file, "lp", FALSE ));
+   /*making sure the solution is feasible*/
+   for (s=0; s<nsols;++s)
+   {
+      SCIP_Bool feasible;
+      SCIP_SOL* sol;
+      //cout<<"\nsolution"<<s<<" is "<<(SCIPgetSolTransObj(scip, sol)-SCIPconsGetnSlotConss(probdata)*lambda);
+
+
+      /* the soultion should be sorted w.r.t. the objective function value */
+      assert(s == 0 || SCIPisFeasGE(relaxscip, SCIPgetSolOrigObj(relaxscip, sols[s-1]), SCIPgetSolOrigObj(relaxscip, sols[s])));
+
+      sol=sols[s];
+      assert(sol!=NULL);
+
+      SCIP_CALL(SCIPcheckSolOrig(relaxscip,sol,&feasible,FALSE,FALSE));
+
+      if (!feasible)
+      {
+         cout<<"solution has a problem";
+         continue;
+      }
+      
+       
+      /*printing the solution of the relaxscip*/
+      SCIP_CALL(SCIPprintSol(relaxscip,sol, file, FALSE));
+      //SCIP_CALL(SCIPprintOrigProblem(relaxscip, file, "lp", FALSE ));
+      //SCIP_CALL(SCIPprintOrigProblem(scip, file, "lp", TRUE ));
+      //SCIP_CALL(SCIPwriteLP(scip,relaxlagr));      
+
+   }
+ /*The next step would be to refine the problem through an iteration. This iteration will be on the dualmultipliers, the subgradients, and the stepsize*/
+   SCIP_Real* subgradients[probdata->nSlotConss];
+   for (int r = 0; r < probdata->nSlotConss; r++)
+   {
+      subgradients[r]==0;
+      SCIP_CONS* cons = SlotConss[r];
+      SCIP_VAR** consvars;
+      int nconsvars;
+      SCIPallocBufferArray(scip,varbuffer,nconsvars);
+      
+      SCIPgetConsNVars(scip,cons,&nconsvars,&success);
+      SCIP_CALL(SCIPgetConsVars(scip,cons,varbuffer,nconsvars,&success));
+      //SCIPgetSolVals(relaxscip,sols[0],nconsvars,varbuffer,vals);
+
+      //SCIP_CALL( SCIPgetVarSols(relaxscip,nconsvars,varbuffer,vals));
+      for(int v=0; v<nconsvars; v++)
+      {
+        const char* consvarname = SCIPconsGetName(varbuffer[v]);
+        int val = SCIPgetVarSol(relaxscip,varbuffer[v]);
+        //SCIP_Real consvarsol = (SCIPgetVarSol(relaxscip,consvar));
+        //SCIP_Real consvarsol =  (relaxscip,sols[0],varbuffer[v]);
+        
+        cout<<"sol for "<<consvarname<<" is "<<val<<"\n";
+        
+      }
+      SCIPfreeBufferArray(scip,varbuffer);
+   }
+   
+
+
+   cout<<"are we done yet?";
+/* The next step will working on finding a better */
+   //SCIPsetMessagehdlrQuiet(relaxscip, TRUE);
+   //SCIP_CALL(SCIPtransformProb(relaxscip));
+   
+   
+
+   //creating variables for the 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/src/relax_lagr.cpp b/src/src/relax_lagr.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..212e44c1c596f0358cdc9d7715d49888c6a75591
--- /dev/null
+++ b/src/src/relax_lagr.cpp
@@ -0,0 +1,955 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  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 = 1.00000;
+   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 = 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]);
+         
+      }
+      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)
+{  
+   /*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 = 1.00000;
+   // 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] = 1;
+   //    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");
+
+   // int maxiter=20;
+   // SCIP_Real lagrdual =-SCIPinfinity(scip); 
+
+   // 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);
+
+   //       for(int t=0;t<NVarInBadConss;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));
+         
+   //    }
+      
+   //    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);
+   //    SCIPdebugMessage("relaxation bound = %e status = %d\n", relaxval, SCIPgetStatus(relaxscip));
+   //    /*get the best solution*/   
+   //    SCIP_SOL* bestsol = SCIPgetBestSol(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,"first iteration \t bound=%f, \t objsol=%f \n",SCIPgetPrimalbound(relaxscip),relaxval);
+   //    SCIP_CALL(SCIPprintBestSol(relaxscip,solutions,FALSE));
+
+   //    /*store the solution in solvals so we can later export it to subgradient function*/
+   //    SCIPgetSolVals(relaxscip,bestsol,nvars,vars,solvals);
+      
+
+   //    stepsize = 1/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];
+         
+   //       SCIP_CONS* cons = SCIPgetConss(relaxscip)[id];
+   //       // SCIP_CALL(SCIPgetConsNVars(relaxscip, cons, &nconsvars, &valid));
+
+   //       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){subgradients[r]==0; }
+   //       else{subgradients[r]==ax;checker++;}
+         
+   //    }
+      
+      
+      
+
+
+   //    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]);
+   //    }
+   //    SCIP_CALL( SCIPfreeTransform(relaxscip) );   
+   //    SCIPinfoMessage(relaxscip, TimeCollector, "\n subgradients found after (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+      
+
+   //    //add back the sum of the duals we subtracted from the main obj function
+
+   //    int sum=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 \n",dualmultipliers[r]);
+   //    }
+   //    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);
+
+
+   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/src/relax_lagr.h b/src/src/relax_lagr.h
new file mode 100644
index 0000000000000000000000000000000000000000..352b5fd8fdb275e97a5f12298d05511f518fec26
--- /dev/null
+++ b/src/src/relax_lagr.h
@@ -0,0 +1,53 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  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.h
+ * @ingroup RELAXATORS
+ * @brief  lagr relaxator
+ * @author Tobias Achterberg
+ */
+
+/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+
+#ifndef __SCIP_RELAX_lagr_H__
+#define __SCIP_RELAX_lagr_H__
+
+
+#include "scip/scip.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** creates the lagr relaxator and includes it in SCIP */
+SCIP_EXPORT
+SCIP_RETCODE SCIPincludeRelaxlagrangian(
+   SCIP*                 scip                /**< SCIP data structure */
+   );
+
+#ifdef __cplusplus
+}
+
+int SCIPvardataGetNVarInBadConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+);
+
+int* SCIPvardataGetvarids(
+	SCIP_VARDATA* vardata     /**< variable data */
+);
+
+#endif
+
+#endif
diff --git a/src/src/relax_lagr32.cpp b/src/src/relax_lagr32.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..61f4b2358b5c4c46c7258324113dd3e769ca7894
--- /dev/null
+++ b/src/src/relax_lagr32.cpp
@@ -0,0 +1,427 @@
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+/*                                                                           */
+/*                  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         500
+#define RELAX_FREQ             1
+
+
+
+
+/*
+ * 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_Real* bestsolvals;
+   SCIP_Real* feasiblesol;
+   SCIP_Real* upperbound;
+};
+
+/** 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) */
+
+static
+SCIP_DECL_RELAXINIT(relaxInitlagr)
+{  /*lint --e{715}*/
+   printf("hellow\n");
+
+   using std::cout;
+   SCIP*                                  relaxscip;                   //a copy of the scip we can work on
+   SCIP_HASHMAP*                          varmap;                      //to map the variables from scip to relaxscip
+   SCIP_HASHMAP*                          consmap;                     //to map the constraints from scip to relaxscip
+
+   SCIP_VARDATA*                           vardata;                     //contains variable data, including in which constraints it's found
+   SCIP_ProbData*                          probdata;                    //contains constraint data, identify slot vs start conss, and also which variables it contains
+   SCIP_RELAXDATA*                         relaxdata;                   // not yet used, but can be part of the struct SCIP_RelaxData
+
+   SCIP_CLOCK*                             varslottime;                 //to help us record the time
+
+   FILE*                                   file;                        
+   
+   SCIP_Bool                               success;
+   SCIP_Bool                               valid;
+
+   SCIP_SOL**                              sols;
+
+   relaxdata = SCIPrelaxGetData(relax);
+   SCIP_CALL(SCIPallocBlockMemory(scip , &relaxdata));
+   assert(relaxdata != NULL);
+
+
+   /*************************************************************************************************/
+   /*we start with creating our relaxed scip instance, where the variable and constraints are mapped*/
+   /*************************************************************************************************/
+   SCIP_CALL( SCIPcreate(&relaxscip));
+   valid=FALSE;
+   SCIP_CALL( SCIPhashmapCreate(&varmap, SCIPblkmem(relaxscip), SCIPgetNVars(scip)) );
+   SCIP_CALL(SCIPcopy(scip,relaxscip,varmap,consmap,"relaxscip",FALSE,FALSE,FALSE,FALSE,&valid));
+   
+   SCIP_VAR** origvars = SCIPgetVars(scip);
+   SCIP_VAR** vars = SCIPgetVars(relaxscip);
+   SCIP_CONS** conss = SCIPgetConss(relaxscip);
+   int nconss = SCIPgetNConss(relaxscip);
+   int nvars = SCIPgetNVars(relaxscip);
+   SCIP_Real upperbound = 0;
+   // printf("**************%f***********",SCIPgetNCountedFeasSubtrees(relaxscip));
+
+   SCIP_CALL( SCIPcreateClock(relaxscip, &varslottime) );                     //* start time counting*  
+   SCIP_CALL(SCIPstartClock(relaxscip,varslottime));
+
+   /**************************************************************************************************************/
+   /*First,                                                                                                      */
+   //*the probdata: where we get to identify the bad constraint we want to formulate(in our case, the slot conss) */
+   //*the vardata: where we will get save the slot constraints on which the variable exists,                       */
+   /*--along with the quotient of the obj to the nVarSlotConss                                                    */
+   /***************************************************************************************************************/
+                                     
+   SCIPcreateprobdata(relaxscip,&probdata);     //*will be used to identify the #of slot constraints 
+   printf("%d", SCIPgetNConss(relaxscip));
+   int nSlotConss = SCIPgetNSlotConss(probdata);
+   printf("%d", SCIPgetNSlotConss(probdata));
+
+   SCIP_Real* origobj;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&origobj,nvars));
+
+
+   for (int v = 0; v < nvars; v++)
+   {
+      SCIP_VAR* var = vars[v];
+      origobj[v]= SCIPvarGetObj(var);
+
+      vardata=NULL;
+      SCIPvardataCreateLagrangian(relaxscip,vardata,&var,nSlotConss,v);        //*we create the vardata, containing the ids of the constraints containing the variable
+      vardata=SCIPvarGetData(var);  
+      if(upperbound<=SCIPvarGetObj(var))
+      {
+         upperbound = SCIPvarGetObj(var);
+      }
+   } 
+   upperbound = upperbound * (nconss - nSlotConss);
+   printf("%f",upperbound);  
+   // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   // SCIPinfoMessage(relaxscip, NULL, "Solving Time (sec) : %6f\n", SCIPgetClockTime(relaxscip, varslottime));
+
+
+   SCIP_SOL* bestsol ;
+   SCIP_Real* dualmultipliers;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&dualmultipliers,nSlotConss));   
+                                           
+   SCIP_Real sumofduals = 0;   
+   SCIP_Real stepsize = 0.0000;
+   SCIP_Real sqsum = 0;
+   SCIP_Real sqrtsum = 0;
+   SCIP_Real C = 2;
+
+
+   SCIP_Real* subgradients;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&subgradients,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       */
+   /******************************************************************************************************************/
+  
+   for ( int r = 0; r < nSlotConss; ++r)
+   {
+            // printf("*******************%f\n",subgradients[r]);
+      SCIP_CONS* cons = conss[r]; 
+   
+      dualmultipliers[r]=SCIPconsGetMultiplier(relaxscip,&cons, 0,0,0,true,0);           //returns the minumum
+      dualmultipliers[r] = 0;
+      sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+      // printf("(%s,%f) \n",SCIPconsGetName(cons), dualmultipliers[r]);
+        
+   }
+
+   
+   int niters = 2250;
+   int stoppingiter=0;
+   
+   /**solvals is used for storing solutions in an iteration. where as bestsolvals will store the solution with the best objective value*/
+   SCIP_Real* solvals;
+   SCIP_Real* bestsolvals;
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&solvals,nvars+1));
+   SCIP_CALL(SCIPallocBufferArray(relaxscip,&bestsolvals,nvars+1+niters));
+
+   bestsolvals[nvars] = -10000000000000000;
+   // printf("%f",bestsolvals[nvars+1]);
+   
+   // SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   // SCIPinfoMessage(relaxscip, NULL, "Solving Time (sec) : %6f\n", SCIPgetClockTime(relaxscip, varslottime));
+    
+   /*add all the duals that are added onto the variabls under sumofaddonvars*/
+   SCIP_Real sumofaddonvars=0;
+   SCIP_Real percent;
+   SCIP_Real percent2;
+   int counter=0;
+
+   SCIP_Real sumofsqsubgrad=0;
+
+   
+   stepsize = 0.25;
+   /*Now we can start with our interation to maximize the dualmultipliers while minimizing the reformulated SCIP instance*/
+   for(int k =  0; k < niters; ++k)
+   {  
+      
+      stepsize = 10*(1/(0.5*(k+1)));
+      // printf("%f\n\n",stepsize);
+      sumofaddonvars = 0;       
+      SCIP_CALL(SCIPfreeTransform(relaxscip));
+      /*******************************************************************************************************/
+      /* 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 nVarSlotConss, and r is nSlotConss for our case *******************************/
+      /*******************************************************************************************************/
+      SCIPvarchangeDuals(relaxscip,&vars,dualmultipliers,origobj);
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,-1*sumofduals));
+      // printf("*****************************upperbound*********************---> %f",SCIPgetUpperbound(relaxscip));
+      // SCIP_CALL(SCIPprintOrigProblem(relaxscip, file, "lp", FALSE));
+      
+      SCIPsolveiteration(relaxscip,nSlotConss,&subgradients,2,&stepsize,&bestsolvals,&upperbound, k);    
+      SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(relaxdata->bestsolvals), bestsolvals, nvars+1));
+      // printf("\n best obj = %f \n",bestsolvals[nvars]);
+
+
+      SCIP_CALL(SCIPaddOrigObjoffset(relaxscip,sumofduals));
+    
+      sumofduals = 0;
+      for ( int r = 0; r < nSlotConss; ++r)
+      {  
+         SCIP_CONS* cons = conss[r]; 
+         // printf("--subgrad %f--",subgradients[r]);
+         SCIP_Real subgradient = subgradients[r];
+         sumofsqsubgrad += subgradients[r]*subgradients[r];
+      } 
+
+      for ( int r = 0; r < nSlotConss; ++r)
+      {  
+         SCIP_CONS* cons = conss[r]; 
+         // printf("--subgrad %f--",subgradients[r]);
+         SCIP_Real subgradient = subgradients[r];
+         dualmultipliers[r]=SCIPconsGetMultiplier(relaxscip,&cons,subgradient,C,stepsize,false,dualmultipliers[r]);           //returns the minumum
+         sumofduals+=dualmultipliers[r];                    //adds the negative of the minimum in each iteration
+         //prinf("(%s,%f, sumofduals %f) \n",SCIPconsGetName(cons), dualmultipliers[r],sumofduals);
+      }
+
+      
+      
+
+      ++stoppingiter;
+      if(bestsolvals[nvars]!=bestsolvals[nvars+k+1]){++counter;}
+      else{counter=0;}
+      if(counter==10){ break; } 
+
+      if((bestsolvals[nvars+k+1])-)
+   }
+
+   SCIP_CALL(SCIPstopClock(relaxscip,varslottime));
+   SCIPinfoMessage(relaxscip, NULL, "Solving Time (sec) : %f\n", SCIPgetClockTime(relaxscip, varslottime));
+
+   FILE* saveniter;
+   saveniter = fopen("values.txt","w");
+   fprintf(saveniter,"%d\n",stoppingiter);
+   fclose(saveniter);
+
+   FILE* savebestsols;
+   for(int v=nvars+1; v<nvars+1+stoppingiter; ++v)
+   {
+      // prinf("(%s bsv= %f\n", SCIPvarGetName(vars[v]),bestsolvals[v]);
+      savebestsols = fopen("values.txt","a");
+      fprintf(savebestsols,"%f\n", bestsolvals[v]);
+      // fputs("This is testing for fputs...\n",were);
+      fclose(savebestsols);
+   }
+
+   /*
+   for(int v=0; v<nvars; ++v)
+   {
+      SCIP_VAR* var = vars[v];
+      // printf("orobj %f|",origobj[v]);
+      // vardata = SCIPvarGetData(var);
+      // SCIP_CALL(SCIPVaraddDualMultiplier(relaxscip,&var,dualmultipliers));
+      // printf("%f",SCIPvarGetQuotient(vardata));
+      // vardataDelete(relaxscip,&vardata);
+   }
+
+
+   */   
+
+   
+   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}*/
+   SCIPerrorMessage("method of lagr relaxator not implemented yet\n");
+   SCIPABORT(); /*lint --e{527}*/
+
+   return SCIP_OKAY;
+}
+#else
+#define relaxExitsollagr NULL
+#endif
+
+
+/** execution method of relaxator */
+static
+SCIP_DECL_RELAXEXEC(relaxExeclagr)
+{  
+   /*lint --e{715}*/
+   
+   // SCIPABORT();
+   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/src/vardata_lagr.cpp b/src/src/vardata_lagr.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..6fa5a07ad87d64d8b8956343e76b12a83d16ec06
--- /dev/null
+++ b/src/src/vardata_lagr.cpp
@@ -0,0 +1,324 @@
+/** 
+ * For the next code, we want to find the constraints that are connected with a certain variable, for example x_1_1. We create three for loops, first one is for the variables, for eg. x_1_1.
+ * after that we start a second loop for the constraints, let's say for C1, and we go through all the variables connected with this cons. Third loop will go through the variable in step 2 
+ * one by one and then compares it with the original var, x_1_1. If true(the indexes are the same), the constrait will be saved in the array designated, constraintarray or better name, varconss. 
+ 
+ * The second part of the loop will be to separate the constraints attached with our variable, for example, c1,c2,F1. For this we create a for loop with size equaling to 3(for the ex.)
+ * we create an if condition that checkes if the cons starts with c or F. If c, then we create and allocate it to an array called "slotconstraintarray", else, "VarStartConssarray".
+ * We assure that one variable is only found in one of the start constraints with an assert function. 
+ * All of these will be saved under the vardata!
+*//*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+
+//#include "probdata_lagr.h"
+#include "vardata_lagr.h"
+#include "probdata_lagr.h"
+#include <chrono>
+
+#include <iostream>
+#include <assert.h>
+struct SCIP_VarData
+{
+   SCIP_VAR*                        var;
+   SCIP_CONS**                      VarConss;
+   int                              nVarConss;
+   SCIP_CONS**                      VarSlotConss;                   /**<contains all slot constraints containing the variable */
+   int                              nVarSlotConss;                 /**<number of slot constraints the variable is occuring in*/  
+   SCIP_Real                        varquotient;
+   int*                              consids;
+   int*                               varids;
+   int                               varid;
+};
+
+SCIP_RETCODE vardataDelete(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_VARDATA**        vardata             /**< vardata to delete */
+   )
+{
+   SCIPfreeBlockMemoryArray(scip, &(*vardata)->consids, (*vardata)->nVarSlotConss);
+   SCIPfreeBlockMemory(scip, vardata);
+
+   return SCIP_OKAY;
+}
+
+/** frees user data of variable */
+SCIP_RETCODE vardatafree(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_VARDATA**        vardata             /**< vardata to delete */
+   )
+{
+   assert(scip != NULL);
+   assert(vardata != NULL);
+   assert(*vardata!=NULL);
+
+  
+   if((*vardata)->VarSlotConss != NULL)
+   {
+      SCIPfreeBlockMemoryArray(scip, &(*vardata)->VarSlotConss, (*vardata)->nVarSlotConss);
+   }
+   SCIPfreeBlockMemory(scip, vardata);
+   
+
+   return SCIP_OKAY;
+}
+
+
+
+/** gets the slot conss the var is occuring*/
+SCIP_CONS** SCIPvardataGetSlotConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+   )
+ {
+    return vardata->VarSlotConss;
+ }
+
+
+/** gets the number of slot conss the var is occuring in*/
+int SCIPvardataGetnVarSlotConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+ {
+    return vardata->nVarSlotConss;
+ }
+
+int* SCIPvardataGetconsids(
+	SCIP_VARDATA* vardata     /**< variable data */
+)
+ {
+    return vardata->consids;
+ }
+
+int SCIPvardataGetVarID(
+        SCIP_VARDATA* vardata     /**< variable data */
+)
+ {
+    return vardata->varid;
+ }
+ 
+/** we add the quotient of each variable, which is equal to: (weight of variable)/(nVarSlotConss)     */
+SCIP_Real SCIPvarGetQuotient(SCIP_VARDATA* vardata)
+{
+
+   return vardata->varquotient;
+}
+
+SCIP_RETCODE SCIPvaridentifier(
+   SCIP*                            scip,                          /**< SCIP data structure*/
+   SCIP_VARDATA*                   vardata,                       /**<pointer to the vardata*/
+   SCIP_VAR**                       var,
+   SCIP_VAR**                       varbuffers,
+   int*                           consids,
+   int*                           badconss,
+   int                              nSlotConss
+)
+{
+   // int varindex = SCIPvarGetIndex(*var);                                    /* (2) */
+   // assert(varindex!= NULL);
+   // int nconsvars = 0;
+   // SCIP_Bool valid;
+   // int* varids;
+   // int nVarSlotConss=0;
+
+   // // printf("%s****%d\n",SCIPvarGetName(var),varindex);
+   // for (int r = 0; r < nSlotConss; ++r)
+   // {
+   //    int id = badconss[r];
+   //    SCIP_CONS* cons = SCIPgetConss(scip)[id];
+   //    // printf("%s \t",SCIPconsGetName(cons));
+   //    SCIP_CALL(SCIPgetConsNVars(scip, cons, &nconsvars, &valid)); 
+   //    SCIP_CALL(SCIPgetConsVars(scip, 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[nVarSlotConss] = cons;
+   //          consids[nVarSlotConss]=id;
+   //          nVarSlotConss++;
+   //          // printf(" %s \t,",SCIPconsGetName(cons));
+   //       }
+   //    }
+   // }
+   // SCIP_CALL(SCIPallocBufferArray(scip, &varids, nVarSlotConss));
+   // for(int t=0;t<nVarSlotConss;t++)
+   // {
+   //    varids[t]=consids[t];
+   //    // printf("%d \t",varid[t]);
+   // }
+
+   // // vardata=SCIPvarGetData(var);
+   // SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+   // SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->varids), varids, nVarSlotConss));
+   // vardata->nVarSlotConss = nVarSlotConss;  /**copy nVarConss to VarData */
+   // // // vardata->varquotient = varquotient;
+   // // vardata->varid = varid;
+   // // /**set the variable data to the variable*/
+   // SCIPvarSetData(*var,vardata);  
+   
+   
+   return SCIP_OKAY;
+}
+
+SCIP_RETCODE SCIPvardataCreateLagrangian(
+   SCIP*                            scip,                          /**< SCIP data structure*/
+   SCIP_VARDATA*                   vardata,                       /**<pointer to the vardata*/
+   SCIP_VAR**                       var,
+   int                            nSlotConss,
+   int                              v
+)
+{
+
+   SCIP_CONS** conss = SCIPgetConss(scip);
+   // int nconss = SCIPgetNConss(scip);
+   int nVarSlotConss = 0;
+   int varid;
+   int* consids;
+   
+   // SCIP_CONS**                      VarSlotConss,                  /**< all slot constraints containing the variable */
+   // int                              nVarSlotConss,                 /**<number of slot constraints the variable is occuring in*/
+   
+   int varindex = SCIPvarGetIndex(*var);                                    /* (2) */
+   assert(varindex!= NULL);
+
+   SCIP_Bool success;
+   SCIP_Real varquotient;
+   // printf("%s {",SCIPvarGetName(*var));                                                      /* (5) */
+
+
+   SCIP_CALL(SCIPallocBufferArray(scip,&consids,nSlotConss));
+  
+   SCIP_VAR** varbuffer;
+
+   for (int r = 0; r < nSlotConss; ++r)
+   {
+      SCIP_CONS* cons = conss[r];
+      if(SCIPconsGetLhs(scip,cons,&success)==-SCIPinfinity(scip))
+      {
+         int nconsvars;
+      /** request number of variables of constraint [c] */
+      
+         SCIP_CALL(SCIPgetConsNVars(scip, cons, &nconsvars, &success));            /* (6) */
+         if (!success)
+         {
+            abort();
+         }
+         //cout<<""<<nconsvars<<"v ";
+         /** allocate memory for the varbuffer arrays of the size of "nconsvars" */
+         SCIP_CALL(SCIPallocBufferArray(scip, &varbuffer, nconsvars));             /* (7) */
+         /** collect constraint variables in array "varbuffer" */
+         SCIP_CALL(SCIPgetConsVars(scip, cons, varbuffer, nconsvars, &success));
+         /** If no success, abort process */
+         if (!success)
+            abort();  
+         
+         /** loop over constraint variables and compare varindices */
+         for (int j = 0; j < nconsvars; ++j)                                            /* (8) */
+         {
+            SCIP_VAR* varx = varbuffer[j];
+            int varbufindex = SCIPvarGetIndex(varx);
+            assert(varbufindex != NULL);
+            
+            /** if var[i] is in cons[c], write conspointer in VarConss and increase nVarConsscounter */
+            if (varindex == varbufindex)                                           /* (9) */
+            {
+               
+               // VarSlotConss[nVarSlotConss] = cons;
+               consids[nVarSlotConss]=r;
+               nVarSlotConss++;
+               // printf("%s,",SCIPconsGetName(cons));
+               
+
+            }
+         }
+      }
+      
+   }
+   
+   if(nVarSlotConss!=0)
+   {
+      varquotient = SCIPvarGetObj(*var)/nVarSlotConss;
+      // printf("(%f)",varquotient);
+
+   }
+   else if(nVarSlotConss ==0)
+   {
+      varquotient =100000000000;
+      //printf("/%f\n",varquotient);
+   }
+
+   varid = v;
+   /** allocate memory for vardata*/
+   SCIP_CALL(SCIPallocBlockMemory(scip , &vardata));     
+   SCIP_CALL(SCIPduplicateBlockMemoryArray(scip, &(vardata->consids), consids, nVarSlotConss));
+   vardata->nVarSlotConss = nVarSlotConss;  /**copy nVarConss to VarData */
+   vardata->varquotient = varquotient;
+   vardata->varid = varid;
+   /**set the variable data to the variable*/
+   SCIPvarSetData(*var,vardata);  
+   // SCIPfreeBufferArray(scip,VarSlotConss);
+   SCIPfreeBufferArray(scip,&consids);
+   
+   // printf("*");
+  
+   return SCIP_OKAY;
+}
+
+
+/*******************************************************************************************************/
+/* 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 nVarSlotConss, 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    */
+/****************************************************************************************************************/
+SCIP_RETCODE SCIPvarchangeDuals(SCIP* relaxscip,SCIP_VAR*** vars, SCIP_Real* dualmultipliers, SCIP_Real* origobj)
+{
+
+   // SCIP_CONS** conss = SCIPgetConss(relaxscip);
+   // int nconss = SCIPgetNConss(relaxscip);
+   SCIP_VARDATA* vardata;
+   int nvars = SCIPgetNVars(relaxscip);
+
+   for(int v = 0; v<nvars; ++v)
+   {
+      SCIPfreeTransform(relaxscip);
+      SCIP_VAR* var = (*vars)[v];  
+      SCIP_Real addonvar = 0;
+
+      SCIPchgVarObj(relaxscip,var,origobj[v]);
+
+      vardata = SCIPvarGetData(var);
+      int* consids;
+      
+      consids = SCIPvardataGetconsids(vardata); 
+      //prinf("\n%s(",SCIPvarGetName(var));
+
+      for(int j= 0; j<SCIPvardataGetnVarSlotConss(vardata);++j)
+      {
+         // printf("%d ",SCIPvardataGetconsids(vardata)[j]);
+         int consid = consids[j];
+         
+         // SCIP_CONS* varcons = conss[consid];
+         //prinf(" (%s with %f) ",SCIPconsGetName(varcons), dualmultipliers[consid]);
+         addonvar += dualmultipliers[consid];
+         
+         
+         
+      }
+      
+      SCIP_CALL(SCIPaddVarObj(relaxscip,var,addonvar));
+      //prinf("**(added %f to %f = %f)**\t",addonvar, origobj[v], SCIPvarGetObj(var));
+      // SCIP_CALL(SCIPaddVarObj(relaxscip,var,addonvar));
+   }
+   return SCIP_OKAY;
+}
diff --git a/src/src/vardata_lagr.h b/src/src/vardata_lagr.h
new file mode 100644
index 0000000000000000000000000000000000000000..171670e388200f8fa8b04d52fcc4e74c270e9a9d
--- /dev/null
+++ b/src/src/vardata_lagr.h
@@ -0,0 +1,106 @@
+/**@file    relax_lagr.cpp
+ * @ingroup Lagrangian
+ * @brief   lagrangian relaxation
+ * @author  Dawit Hailu
+ */
+
+/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
+
+#ifndef __SCIP_VARDATA_LAGR__
+#define __SCIP_VARDATA_LAGR__
+
+#include "scip/scip.h" 
+
+/** create variable data, in the future I will add the delay array and delay of each var*/
+SCIP_RETCODE SCIPvardataCreateLagrangian(
+   SCIP*                            scip,                          /**< SCIP data structure*/
+   SCIP_VARDATA*                    vardata,                       /**<pointer to the vardata*/
+   SCIP_VAR**                       vars,
+   // SCIP_CONS**                      VarSlotConss,                  /**< all slot constraints containing the variable */
+   // int                              nVarSlotConss,                 /**<number of slot constraints the variable is occuring in*/
+   int                              nSlotConss,
+   int                              v
+
+);
+
+
+SCIP_RETCODE SCIPvaridentifier(
+   SCIP*                            scip,                          /**< SCIP data structure*/
+   SCIP_VARDATA*                   vardata,                       /**<pointer to the vardata*/
+   SCIP_VAR**                       var,
+   SCIP_VAR**                       varbuffers,
+   int*                           consids,
+   int*                           badconss,
+   int                              nSlotConss
+);
+/** gets the slot conss the var is occuring*/
+SCIP_CONS** SCIPvardataGetSlotConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+   );
+
+/** gets the number of slot conss the var is occuring in*/
+int SCIPvardataGetnVarSlotConss(
+	SCIP_VARDATA* vardata     /**< variable data */
+);
+
+/** gets the ids of the slotconss the var is occuring*/
+int* SCIPvardataGetconsids(
+	SCIP_VARDATA* vardata     /**< variable data */
+);
+
+SCIP_Real SCIPvarGetQuotient(
+   SCIP_VARDATA* vardata
+);
+
+SCIP_RETCODE vardataDelete(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_VARDATA**        vardata             /**< vardata to delete */
+   );
+/** prints vardata to file stream */
+void SCIPvardataPrint(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_VARDATA*         vardata,            /**< variable data */
+   FILE*                 file                /**< the text file to store the information into */
+);
+
+
+SCIP_RETCODE vardataCreate(
+   SCIP*                scip,                     /**< SCIP data structure */
+   SCIP_VARDATA**       vardata,                  /**< pointer to vardata */
+   SCIP_CONS**          VarSlotConss,             /**< array of constraints ids */
+   int                  nVarSlotConss,             /**< number of constraints */
+   SCIP_Real            varquotient
+);  
+
+/** frees user data of variable */
+SCIP_RETCODE vardatafree(
+   SCIP*                 scip,               /**< SCIP data structure */
+   SCIP_VARDATA**        vardata             /**< vardata to delete */
+   );
+
+SCIP_RETCODE lagrVarObjoverNVarslotConss (
+   SCIP_VARDATA*** vardata,
+   SCIP_VAR** var
+);
+
+SCIP_Real SCIPVaraddDualMultiplier(
+   SCIP* scip,
+   SCIP_VAR** var,
+   SCIP_Real* dualmultipliers,
+   SCIP_PROBDATA* probdata
+);
+
+int SCIPvardataGetVarID(
+	SCIP_VARDATA* vardata     /**< variable data */
+);
+
+SCIP_RETCODE SCIPvarchangeDuals(
+   SCIP* relaxscip,
+   SCIP_VAR*** vars, 
+   SCIP_Real* dualmultipliers,
+   SCIP_Real* origobj
+);
+
+
+
+#endif
diff --git a/values.txt b/values.txt
index b92f50489b9ac17f58680f6262c2570bae080d74..3457fa0a5bb1127538db607eb59472338561f129 100644
--- a/values.txt
+++ b/values.txt
@@ -1,585 +1,12 @@
-584
-969553.451655
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-969775.061160
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