// Copyright (C) 2007, International Business Machines
// Corporation and others.  All Rights Reserved.
/*
  This example shows the creation of a model from arrays, solution
  and then changes to objective and adding a row
*/

#include "CbcModel.hpp"

// Using as solver
#include "OsiClpSolverInterface.hpp"

int main (int argc, const char *argv[])
{
  // model is as exmip1.mps from Data/samples
  int numberRows=5;
  int numberColumns=8;
  int numberElements=14;
  // matrix data - column ordered
  CoinBigIndex start[9]={0,2,4,6,8,10,11,12,14};
  int length[8]={2,2,2,2,2,1,1,2};
  int rows[14]={0,4,0,1,1,2,0,3,0,4,2,3,0,4};
  double elements[14]={3,5.6,1,2,1.1,1,-2,2.8,-1,1,1,-1.2,-1,1.9};
  CoinPackedMatrix matrix(true,numberRows,numberColumns,numberElements,elements,rows,start,length);
    
  // rim data
  double objective[8]={1,0,0,0,2,0,0,-1};
  double rowLower[5]={2.5,-COIN_DBL_MAX,4,1.8,3};
  double rowUpper[5]={COIN_DBL_MAX,2.1,4,5,15};
  double colLower[8]={2.5,0,0,0,0.5,0,0,0};
  double colUpper[8]={COIN_DBL_MAX,4.1,1,1,4,COIN_DBL_MAX,COIN_DBL_MAX,4.3};
  OsiClpSolverInterface solver1;
  // load problem
  solver1.loadProblem(matrix,colLower,colUpper,objective,
			 rowLower,rowUpper);
  // mark integer 
  solver1.setInteger(2);
  solver1.setInteger(3);

  // Solve
  solver1.initialSolve();

  // Pass data and solver to CbcModel 
  CbcModel model(solver1);

  // reduce printout
  model.setLogLevel(1);
  model.solver()->setHintParam(OsiDoReducePrint,true,OsiHintTry);
  // Do complete search
  model.branchAndBound();
  /* Print solution.  CbcModel clones solver so we
     need to get current copy */
    
  const double * solution = model.solver()->getColSolution();
  int i;
    
  for (i=0;i<numberColumns;i++) {
    double value=solution[i];
    if (fabs(value)>1.0e-7&&model.solver()->isInteger(i)) 
      printf("i %d has value %g\n",i,value);
    else if (fabs(value)>1.0e-7) 
      printf("c %d has value %g\n",i,value);
  }

  // Change objective
  solver1.setObjCoeff(0,-100.0);

  // Now model has too much information e.g. best solution
  // simplest is to start again
  // Pass data and solver to CbcModel 
  model = CbcModel(solver1);

  // reduce printout
  model.setLogLevel(1);
  model.solver()->setHintParam(OsiDoReducePrint,true,OsiHintTry);
  // Do complete search
  model.branchAndBound();

  solution = model.solver()->getColSolution();
    
  for (i=0;i<numberColumns;i++) {
    double value=solution[i];
    if (fabs(value)>1.0e-7&&model.solver()->isInteger(i)) 
      printf("i %d has value %g\n",i,value);
    else if (fabs(value)>1.0e-7) 
      printf("c %d has value %g\n",i,value);
  }

  // Add constraint
  int column[8]={0,1,2,3,4,5,6,7};
  double element2[8] ={1,1,1,1,1,1,1,1};
  solver1.addRow(8,column,element2,7.8,COIN_DBL_MAX);

  // Now model has too much information e.g. best solution
  // simplest is to start again
  // Pass data and solver to CbcModel 
  model = CbcModel(solver1);

  // reduce printout
  model.setLogLevel(1);
  model.solver()->setHintParam(OsiDoReducePrint,true,OsiHintTry);
  // Do complete search
  model.branchAndBound();

  solution = model.solver()->getColSolution();
    
  for (i=0;i<numberColumns;i++) {
    double value=solution[i];
    if (fabs(value)>1.0e-7&&model.solver()->isInteger(i)) 
      printf("i %d has value %g\n",i,value);
    else if (fabs(value)>1.0e-7) 
      printf("c %d has value %g\n",i,value);
  }
  return 0;
}    
