1 files changed, 115 insertions, 0 deletions
diff --git a/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp b/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp
new file mode 100644
index 0000000..8292ab1
--- /dev/null
+++ b/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp
@@ -0,0 +1,115 @@
+// 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;
+}
+
+}