// Copyright (C) 2003, International Business Machines
// Corporation and others.  All Rights Reserved.

#include "ClpSimplex.hpp"
#include "CoinSort.hpp"
#include <iomanip>

int main (int argc, const char *argv[])
{
  ClpSimplex  model;
  int status;
  // Keep names
  if (argc<2) {
    status=model.readMps("small.mps",true);
  } else {
    status=model.readMps(argv[1],true);
  }
  if (status)
    exit(10);
  /*
    This driver implements what I called Sprint.  Cplex calls it 
    "sifting" which is just as silly.  When I thought of this trivial idea
    it reminded me of an LP code of the 60's called sprint which after
    every factorization took a subset of the matrix into memory (all
    64K words!) and then iterated very fast on that subset.  On the
    problems of those days it did not work very well, but it worked very
    well on aircrew scheduling problems where there were very large numbers
    of columns all with the same flavor.
  */

  /* The idea works best if you can get feasible easily.  To make it
     more general we can add in costed slacks */

  int originalNumberColumns = model.numberColumns();
  int numberRows = model.numberRows();

  // We will need arrays to choose variables.  These are too big but ..
  double * weight = new double [numberRows+originalNumberColumns];
  int * sort = new int [numberRows+originalNumberColumns];
  int numberSort=0;
  // Say we are going to add slacks - if you can get a feasible
  // solution then do that at the comment - Add in your own coding here
  bool addSlacks = true;

  if (addSlacks) {
    // initial list will just be artificials
    // first we will set all variables as close to zero as possible
    int iColumn;
    const double * columnLower = model.columnLower();
    const double * columnUpper = model.columnUpper();
    double * columnSolution = model.primalColumnSolution();

    for (iColumn=0;iColumn<originalNumberColumns;iColumn++) {
      double value =0.0;
      if (columnLower[iColumn]>0.0)
	value = columnLower[iColumn];
      else if (columnUpper[iColumn]<0.0)
	value = columnUpper[iColumn];
      columnSolution[iColumn]=value;
    }
    // now see what that does to row solution
    double * rowSolution = model.primalRowSolution();
    memset (rowSolution,0,numberRows*sizeof(double));
    model.times(1.0,columnSolution,rowSolution);

    int * addStarts = new int [numberRows+1];
    int * addRow = new int[numberRows];
    double * addElement = new double[numberRows];
    const double * lower = model.rowLower();
    const double * upper = model.rowUpper();
    addStarts[0]=0;
    int numberArtificials=0;
    double * addCost = new double [numberRows];
    const double penalty=1.0e8;
    int iRow;
    for (iRow=0;iRow<numberRows;iRow++) {
      if (lower[iRow]>rowSolution[iRow]) {
	addRow[numberArtificials]=iRow;
	addElement[numberArtificials]=1.0;
	addCost[numberArtificials]=penalty;
	numberArtificials++;
	addStarts[numberArtificials]=numberArtificials;
      } else if (upper[iRow]<rowSolution[iRow]) {
	addRow[numberArtificials]=iRow;
	addElement[numberArtificials]=-1.0;
	addCost[numberArtificials]=penalty;
	numberArtificials++;
	addStarts[numberArtificials]=numberArtificials;
      }
    }
    model.addColumns(numberArtificials,NULL,NULL,addCost,
		      addStarts,addRow,addElement);
    delete [] addStarts;
    delete [] addRow;
    delete [] addElement;
    delete [] addCost;
    // Set up initial list
    numberSort=numberArtificials;
    int i;
    for (i=0;i<numberSort;i++)
      sort[i] = i+originalNumberColumns;
  } else {
    // Get initial list in some magical way
    // Add in your own coding here
    abort();
  }

  int numberColumns = model.numberColumns();
  const double * columnLower = model.columnLower();
  const double * columnUpper = model.columnUpper();
  double * fullSolution = model.primalColumnSolution();

  // Just do this number of passes
  int maxPass=100;
  int iPass;
  double lastObjective=1.0e31;

  // Just take this number of columns in small problem
  int smallNumberColumns = CoinMin(3*numberRows,numberColumns);
  smallNumberColumns = CoinMax(smallNumberColumns,3000);
  // We will be using all rows
  int * whichRows = new int [numberRows];
  for (int iRow=0;iRow<numberRows;iRow++)
    whichRows[iRow]=iRow;
  double originalOffset;
  model.getDblParam(ClpObjOffset,originalOffset);

  for (iPass=0;iPass<maxPass;iPass++) {
    printf("Start of pass %d\n",iPass);
    //printf("Bug until submodel new version\n");
    CoinSort_2(sort,sort+numberSort,weight);
    // Create small problem
    ClpSimplex small(&model,numberRows,whichRows,numberSort,sort);
    // now see what variables left out do to row solution
    double * rowSolution = model.primalRowSolution();
    memset (rowSolution,0,numberRows*sizeof(double));
    int iRow,iColumn;
    // zero out ones in small problem
    for (iColumn=0;iColumn<numberSort;iColumn++) {
      int kColumn = sort[iColumn];
      fullSolution[kColumn]=0.0;
    }
    // Get objective offset
    double offset=0.0;
    const double * objective = model.objective();
    for (iColumn=0;iColumn<originalNumberColumns;iColumn++) 
      offset += fullSolution[iColumn]*objective[iColumn];
    small.setDblParam(ClpObjOffset,originalOffset-offset);
    model.times(1.0,fullSolution,rowSolution);

    double * lower = small.rowLower();
    double * upper = small.rowUpper();
    for (iRow=0;iRow<numberRows;iRow++) {
      if (lower[iRow]>-1.0e50) 
	lower[iRow] -= rowSolution[iRow];
      if (upper[iRow]<1.0e50)
	upper[iRow] -= rowSolution[iRow];
    }
    /* For some problems a useful variant is to presolve problem.
       In this case you need to adjust smallNumberColumns to get
       right size problem.  Also you can dispense with creating
       small problem and fix variables in large problem and do presolve
       on that. */
    // Solve 
    small.primal();
    // move solution back
    const double * solution = small.primalColumnSolution();
    for (iColumn=0;iColumn<numberSort;iColumn++) {
      int kColumn = sort[iColumn];
      model.setColumnStatus(kColumn,small.getColumnStatus(iColumn));
      fullSolution[kColumn]=solution[iColumn];
    }
    for (iRow=0;iRow<numberRows;iRow++) 
      model.setRowStatus(iRow,small.getRowStatus(iRow));
    memcpy(model.primalRowSolution(),small.primalRowSolution(),
	   numberRows*sizeof(double));
    if ((small.objectiveValue()>lastObjective-1.0e-7&&iPass>5)||
	!small.numberIterations()||
	iPass==maxPass-1) {

      break; // finished
    } else {
      lastObjective = small.objectiveValue();
      // get reduced cost for large problem
      // this assumes minimization
      memcpy(weight,model.objective(),numberColumns*sizeof(double));
      model.transposeTimes(-1.0,small.dualRowSolution(),weight);
      // now massage weight so all basic in plus good djs
      for (iColumn=0;iColumn<numberColumns;iColumn++) {
	double dj = weight[iColumn];
	double value = fullSolution[iColumn];
	if (model.getColumnStatus(iColumn)==ClpSimplex::basic)
	  dj = -1.0e50;
	else if (dj<0.0&&value<columnUpper[iColumn])
	  dj = dj;
	else if (dj>0.0&&value>columnLower[iColumn])
	  dj = -dj;
	else if (columnUpper[iColumn]>columnLower[iColumn])
	  dj = fabs(dj);
	else
	  dj = 1.0e50;
	weight[iColumn] = dj;
	sort[iColumn] = iColumn;
      }
      // sort
      CoinSort_2(weight,weight+numberColumns,sort);
      numberSort = smallNumberColumns;
    }
  }
  if (addSlacks) {
    int i;
    int numberArtificials = numberColumns-originalNumberColumns;
    for (i=0;i<numberArtificials;i++)
      sort[i] = i + originalNumberColumns;
    model.deleteColumns(numberArtificials,sort);
  }
  delete [] weight;
  delete [] sort;
  delete [] whichRows;
  model.primal(1);
  return 0;
}    
