1 files changed, 0 insertions, 115 deletions
diff --git a/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp b/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp
deleted file mode 100644
index 8292ab1..0000000
--- a/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp
+++ /dev/null
@@ -1,115 +0,0 @@
-// MILP with CBC library, mps
-// Finds the solution by using CBC Library
-// Code Authors: Akshay Miterani and Pranav Deshpande
-
-#include <sci_iofunc.hpp>
-
-// For Branch and bound
-#include "OsiSolverInterface.hpp"
-#include "CbcModel.hpp"=
-#include "CbcCutGenerator.hpp"
-#include "CbcHeuristicLocal.hpp"
-#include "OsiClpSolverInterface.hpp"
-extern "C" {
-#include <api_scilab.h>
-
-int mps_cppintlinprog()
-{
-    OsiClpSolverInterface solver;  
-    
-    // Path to the MPS file
-    char *mpsFilePath;
-
-    // Options to set maximum iterations
-    double *options;
-
-    // Input - 1 or 2 arguments allowed.
-    CheckInputArgument(pvApiCtx, 2, 2);
-
-    // Get the MPS File Path from Scilab
-    getStringFromScilab(1, &mpsFilePath);
-    
-    // Receive the options for setting the maximum number of iterations etc.
-    if( getFixedSizeDoubleMatrixFromScilab(2, 1, 4, &options))
-    {
-        return 1;
-    }
-    
-    // Read the MPS file
-    solver.readMps(mpsFilePath);
-
-    // Cbc Library used from here
-    CbcModel model(solver);
-
-    model.solver()->setHintParam(OsiDoReducePrint, true, OsiHintTry);
-    
-    if((int)options[0]!=0)
-            model.setIntegerTolerance(options[0]);
-    if((int)options[1]!=0)
-            model.setMaximumNodes((int)options[1]); 
-    if((int)options[2]!=0)
-            model.setMaximumSeconds(options[2]);
-    if((int)options[3]!=0)
-            model.setAllowableGap(options[3]);
-    
-    model.branchAndBound();
-
-    int nVars = model.getNumCols();
-    int nCons = model.getNumRows();
-    
-    const double *val = model.getColSolution();
-    
-    //Output the solution to Scilab
-    
-    //get solution for x
-    double* xValue = model.getColSolution();
-
-    //get objective value
-    double objValue = model.getObjValue();
-
-    //Output status
-    double status_=-1;
-    if(model.isProvenOptimal()){
-        status_=0;
-    }
-    else if(model.isProvenInfeasible()){
-        status_=1;
-    }
-    else if(model.isSolutionLimitReached()){
-        status_=2;
-    }
-    else if(model. isNodeLimitReached()){
-        status_=3;
-    }
-    else if(model.isAbandoned()){
-        status_=4;
-    }
-    else if(model.isSecondsLimitReached()){
-        status_=5;
-    }
-    else if(model.isContinuousUnbounded()){
-        status_=6;
-    }
-    else if(model.isProvenDualInfeasible()){
-        status_=7;
-    }
-
-    double nodeCount = model.getNodeCount();
-    double nfps = model.numberIntegers();
-    double U = model.getObjValue();
-    double L = model.getBestPossibleObjValue();
-    double iterCount = model.getIterationCount();
-
-    returnDoubleMatrixToScilab(1 , nVars, 1 , xValue);
-    returnDoubleMatrixToScilab(2 , 1 , 1 , &objValue);
-    returnDoubleMatrixToScilab(3 , 1 , 1 , &status_);
-    returnDoubleMatrixToScilab(4 , 1 , 1 , &nodeCount);
-    returnDoubleMatrixToScilab(5 , 1 , 1 , &nfps);
-    returnDoubleMatrixToScilab(6 , 1 , 1 , &L);
-    returnDoubleMatrixToScilab(7 , 1 , 1 , &U);
-    returnDoubleMatrixToScilab(8 , 1 , 1 , &iterCount);
-
-    return 0;
-}
-
-}