/* initialisiere N^2 Variablen und erstelle eine zu minimierende
* Strecken-Zielfunktion über die Distanzmatrix c
*
* eigentlich sind nur (N*N-N)/2 Variablen nötig, aber dafür müsste
* ich mir etwas schlaues zur Adressierung ausdenken (weil das untere linke
* Dreieck einer Matrix adressiert werden muss, ist das nicht trivial)
* Der Presolver scheint die überflüssigen Variablen allerdings
* direkt zu verwerfen, weshalb das nicht dringend ist.
* */
IloNumVarArray CplexTSPSolver::init_symmetric_var(IloModel model)
{
IloEnv env = model.getEnv();
// Edge Variables
IloNumVarArray x(env);
for(int i=0; i<N; i++)
for(int j=0; j<N; j++)
if(j<i)
x.add(IloNumVar(env, 0, 1, mip ? ILOINT : ILOFLOAT));
else
x.add(IloNumVar(env, 0, 0, ILOFLOAT)); // fülle oben rechts mit dummies
model.add(x);
// Cost Function
IloExpr expr(env);
// die folgenden Schleifen adressieren ein unteres linkes
// Dreieck in einer quadratischen Matrix
for(int i=0; i<N; i++)
for (int j=0; j<i; j++)
expr += c[i*N + j] * x[i*N + j];
model.add(IloMinimize(env, expr));
expr.end();
return x;
}
void MILPSolverCPX::addCol(const vector<pair<int,double> > & entries, const double & lb, const double & ub, const ColumnType & type)
{
static const bool debug = false;
if (debug) {
cout << "Adding column to LP: ";
}
if (alwaysLPRelaxation) {
(*modelvar).add(IloNumVar(*env, lb, ub));
} else {
if (type == C_BOOL) {
(*modelvar).add(IloNumVar(*env, lb, ub, ILOBOOL));
//cout << "Adding column " << colCount << " as binary\n";
integervars.insert(make_pair(colCount, true));
} else if (type == C_INT) {
(*modelvar).add(IloNumVar(*env, lb, ub, ILOINT));
//cout << "Adding column " << colCount << " as an integer\n";
integervars.insert(make_pair(colCount, false));
} else {
(*modelvar).add(IloNumVar(*env, lb, ub));
}
}
const int entCount = entries.size();
for (int i = 0; i < entCount; ++i) {
IloRange & target = modelcon->data[entries[i].first];
target.setLinearCoef((*modelvar)[colCount], entries[i].second);
coeffs[entries[i].first][colCount] = entries[i].second;
if (debug) {
if (i) cout << " + ";
cout << entries[i].second << "*" << getRowName(entries[i].first);
}
}
if (debug) {
if (lb == -IloInfinity) {
cout << " <= " << ub << "\n";
} else if (ub == IloInfinity) {
cout << " >= " << lb << "\n";
} else if (ub == lb) {
cout << " == " << ub << "\n";
} else {
cout << " in [" << lb << "," << ub << "]\n";
}
}
++colCount;
}
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, -1.0, 1.0));
x.add(IloNumVar(env, 0.0, 1.0));
model.add(IloMinimize(env, 0.5 * (-3*x[0]*x[0] - 3*x[1]*x[1] +
- 1*x[0]*x[1] ) ));
c.add( - x[0] + x[1] >= 0);
c.add( x[0] + x[1] >= 0);
model.add(c);
} // END populatebyrow
void profit_create_vars(graph g, IloModel model, IloNumVarArray x, IloNumVarArray p, IloNumVarArray z, int **columns) {
IloEnv env = model.getEnv();
string s;
assignment_vars(g, model, x, columns);
for(int j = 0; j < g->items; j++) {
s = "p_" + itos(j);
p.add(IloNumVar(env, 0.0, boundp(j), ILOFLOAT, s.c_str()));
// p.add(IloNumVar(env, lowerp(j), boundp(j), ILOFLOAT, s.c_str()));
}
for(int i = 0; i < g->bidders; i++) {
s = "z_" + itos(i);
z.add(IloNumVar(env, 0.0, boundu(i), ILOFLOAT, s.c_str()));
}
}
int
main()
{
IloEnv env;
try {
IloModel model(env);
setData(env);
IloNumVarArray inside(env, nbProds);
IloNumVarArray outside(env, nbProds);
IloObjective obj = IloAdd(model, IloMinimize(env));
// Must meet demand for each product
for(IloInt p = 0; p < nbProds; p++) {
IloRange demRange = IloAdd(model,
IloRange (env, demand[p], demand[p]));
inside[p] = IloNumVar(obj(insideCost[p]) + demRange(1));
outside[p] = IloNumVar(obj(outsideCost[p]) + demRange(1));
}
// Must respect capacity constraint for each resource
for(IloInt r = 0; r < nbResources; r++)
model.add(IloScalProd(consumption[r], inside) <= capacity[r]);
IloCplex cplex(env);
cplex.extract(model);
cplex.solve();
if (cplex.getStatus() != IloAlgorithm::Optimal)
cout << "No optimal solution" << endl;
cout << "Solution status: " << cplex.getStatus() << endl;
displayResults(cplex, inside, outside);
cout << "----------------------------------------" << endl;
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
void addInitColumn(IloNumVarArray lambda, IloObjective rmpObj,
IloRangeArray maintConEng, IloRangeArray removeMod, IloRangeArray convex,
IloNumArray2 addXCol, IloNumArray addZCol,
const IloNumArray compCosts, const IloNumArray convexityCoef) {
// loop counter
IloInt t;
// counter for objective function coefficient for lambda
// variable to be added.
IloNum lambdaObjCoef = 0;
// function assumes addXCol and addZCol contains proper values.
// calculate objective function coefficient lambdaObjCoef
for (t = 0; t < TIME_SPAN; t++) {
// for each fixed t: scalar product of x[m]* vector and
// component costs vector compCosts[m]:
lambdaObjCoef += IloScalProd(addXCol[t],compCosts);
// also clear the addXCol subarrays as soon as they
// have been used
addXCol[t].clear();
}
// now add this column and it's associated lambda variable to the RMP.
lambda.add(IloNumVar(rmpObj(lambdaObjCoef) + maintConEng(addZCol) +
removeMod(addZCol) + convex(convexityCoef), 0.0, 1.0));
// clear addZCol num array.
addZCol.clear();
} // END of addColumn
void addColumn(IloCplex subSolver, IloNumVarArray2 x, IloNumVarArray z, IloNumVarArray lambda,
IloObjective rmpObj, IloRangeArray maintConEng, IloRangeArray removeMod,
IloRangeArray convex, IloNumArray2 addXCol, IloNumArray addZCol,
const IloNumArray compCosts, const IloNumArray convexityCoef) {
// loop counter
IloInt t;
// counter for objective function coefficient for lambda
// variable to be added.
IloNum lambdaObjCoef = 0;
// extract subproblem-optimal solution values
// (into IloNumArrays addXCol (2d) and addZCol (1d)).
// z values:
subSolver.getValues(addZCol,z);
//cout << endl << endl << "z = " << endl << addZCol << endl;
//cin.get();
// !!! OBS !!!
// here we might want to save these z values some column pool's custom-nitted class
// array. Or to be specific, we want to add the indexes for NON-ZERO-ENTRIES in addZCol
// to our class that keep place of columns.
// E.g., given variable lambda(m)_(q_m), we want to know in our own class object,
// given (m)(q_m), the indexes of non-zeros in that Z column.
// and for each t...
for (t = 0; t < TIME_SPAN; t++) {
// x values:
subSolver.getValues(addXCol[t],x[t]);
//cout << endl << endl << "x[t=" << t << "] =" << endl << addXCol[t] << endl;
}
//cin.get();
// calculate objective function coefficient lambdaObjCoef
for (t = 0; t < TIME_SPAN; t++) {
// for each fixed t: scalar product of x[m]* vector and
// component costs vector compCosts[m]:
lambdaObjCoef += IloScalProd(addXCol[t],compCosts);
// also clear the addXCol subarrays as soon as they
// have been used
addXCol[t].clear();
}
// now add this column and it's associated lambda variable to the RMP.
lambda.add(IloNumVar(rmpObj(lambdaObjCoef) + maintConEng(addZCol) +
removeMod(addZCol) + convex(convexityCoef), 0.0, 1.0));
// clear addZCol num array.
addZCol.clear();
} // END of addColumn
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env));
x.add(IloNumVar(env));
x.add(IloNumVar(env, 2.0, 3.0, ILOINT));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2] + x[3]));
c.add( - x[0] + x[1] + x[2] + 10 * x[3] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
c.add( x[1] - 3.5* x[3] == 0);
model.add(c);
} // END populatebyrow
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env));
x.add(IloNumVar(env));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2]
- 0.5 * (33*x[0]*x[0] + 22*x[1]*x[1] +
11*x[2]*x[2] - 12*x[0]*x[1] -
23*x[1]*x[2] ) ));
c.add( - x[0] + x[1] + x[2] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
model.add(c);
} // END populatebyrow
void PopulateFromGraph(IloModel model, IloNumVarArray s, IloRangeArray c){
IloEnv env = model.getEnv();
// Used n+1 for accomodating an extra variable
// For being able to write the objective function
for(int i = 0; i <= n; i++)
s.add(IloNumVar(env));
}
void CPLEXSolver::add_in_constraint(LinearConstraint *con, double coef){
DBG("Creating a Gurobi representation of a constriant %s\n", "");
IloNumArray weights(*env, (IloInt)con->_coefficients.size());
IloNumVarArray vars(*env, (IloInt)con->_variables.size());
for(unsigned int i = 0; i < con->_variables.size(); ++i){
DBG("\tAdding variable to CPLEX\n%s", "");
IloNumVar var_ptr;
if(con->_variables[i]->_var == NULL){
IloNumVar::Type type;
if(con->_variables[i]->_continuous) type = IloNumVar::Float;
// else if(con->_lower == 0 && con->_upper == 1) type = IloNumVar::Bool;
else type = IloNumVar::Int;
var_ptr = IloNumVar(getEnv(),
con->_variables[i]->_lower, // LB
con->_variables[i]->_upper, // UB
type);
int *var_id = new int;
*var_id = variables->getSize();
variables->add(var_ptr);
con->_variables[i]->_var = (void*) var_id;
DBG("Created new variable with id %d. type:%c lb:%f ub:%f coef:%f\n", *var_id, type, con->_variables[i]->_lower, con->_variables[i]->_upper, coef);
} else {
var_ptr = (*variables)[*(int*)(con->_variables[i]->_var)];
}
vars[i] = (*variables)[*(int*)(con->_variables[i]->_var)];
weights[i] = con->_coefficients[i];
}
IloNumExprArg lin_expr = IloScalProd(weights, vars);
if(coef < -0.1){
model->add(IloMinimize(*env, lin_expr));
} else if(coef > 0.1){
model->add(IloMaximize(*env, lin_expr));
} else {
if(con->_lhs > -INFINITY && con->_rhs < INFINITY){
if(con->_lhs == con->_rhs) {
model->add(lin_expr == con->_lhs);
} else {
model->add(IloRange(*env, con->_lhs, lin_expr, con->_rhs));
}
} else if(con->_lhs > -INFINITY) model->add(lin_expr >= con->_lhs);
else if(con->_rhs < INFINITY) model->add(lin_expr <= con->_rhs);
}
}
static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
x.add(IloNumVar(env, 0.0, 40.0));
x.add(IloNumVar(env, 0.0, IloInfinity, ILOINT));
x.add(IloNumVar(env, 0.0, IloInfinity, ILOINT));
x.add(IloNumVar(env, 2.0, 3.0, ILOINT));
model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2] + x[3]));
c.add( - x[0] + x[1] + x[2] + 10 * x[3] <= 20);
c.add( x[0] - 3 * x[1] + x[2] <= 30);
c.add( x[1] - 3.5* x[3] == 0);
model.add(c);
IloNumVarArray sosvar(env, 2);
IloNumArray sosval(env, 2);
sosvar[0] = x[2]; sosvar[1] = x[3];
sosval[0] = 25.0; sosval[1] = 18.0;
model.add(IloSOS1(model.getEnv(), sosvar, sosval));
} // END populatebyrow
static void
populatebycolumn (IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
IloObjective obj = IloMaximize(env);
c.add(IloRange(env, -IloInfinity, 20.0));
c.add(IloRange(env, -IloInfinity, 30.0));
x.add(IloNumVar(obj(1.0) + c[0](-1.0) + c[1]( 1.0), 0.0, 40.0));
x.add(obj(2.0) + c[0]( 1.0) + c[1](-3.0));
x.add(obj(3.0) + c[0]( 1.0) + c[1]( 1.0));
model.add(obj);
model.add(c);
} // END populatebycolumn
bool MIQPSolver::addContig(const Contig &contig)
{
int id = contig.GetID();
try
{
len[id] = contig.Sequence.Nucleotides.length();
optimized[id] = false;
x.add(IloNumVar(environment, 0, CoordMax));
u.add(IloBoolVar(environment));
t.add(IloBoolVar(environment));
constraints.add(t[id] - u[id] <= 0);
}
catch (...)
{
return false;
}
return true;
}
DataVarNumMatrix::DataVarNumMatrix(string name, double borne, IloEnv env,int n, int m){
this->Matrix = IloArray<IloNumVarArray> (env,n);
for(int i = 0; i < n; i++){
this->Matrix[i] = IloNumVarArray(env,m,0,borne);
for(int j = 0; j < m; j++){
string var_name = name;//attribut à nom de la forme x(i,j) à la variable
var_name += "(";
var_name += U::to_s(i+1);
var_name += ",";
var_name += U::to_s(j+1);
var_name += ")";
this->Matrix[i][j] = IloNumVar(env,0,borne,var_name.c_str());
}
}
this->n = n;
this->m = m;
}
int CProblem::setModel()
{
//time_t start, end;
IloEnv env;
try
{
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n); //
for (unsigned int u = 0; u < n; u++)
{
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u] = IloNumVar(env, str.c_str());
}
IloIntVarArray z0(env, Info.numAddVar);
for (int i = 0; i < Info.numAddVar; i++)
{
std::stringstream ss;
ss << i;
std::string str = "z0_" + ss.str();
z0[i] = IloIntVar(env, 0, 1, str.c_str());
}
/* Objective*/
model.add(IloMinimize(env, lambda));
/*Constrains*/
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env, n);
for (unsigned int u = 0; u < n; u++)
{
Par_d[u] = IloNumArray(env, n);
for (unsigned int v = 0; v < n; v++)
{
Par_d[u][v] = d[u][v];
}
}
int M = (max_distance + 1) * n;
for (int i = 0; i < Info.numAddVar; i++)
{
int u = Info.ConstrIndex[i * 2];
int v = Info.ConstrIndex[i * 2 + 1];
model.add(c[u] - c[v] + M * (z0[i]) >= Par_d[u][v]);
model.add(c[v] - c[u] + M * (1 - z0[i]) >= Par_d[u][v]);
}
// d(x) = 3 - x
if (max_distance == 2)
{
// Gridgraphen 1x2 (6 Knoten)
for (int i = 0; i < sqrt(n)-1; i+=1)
{
for (int j = 0; j < sqrt(n)-2; j+=2)
{
model.add(c[i * sqrt(n) + j] + c[i * sqrt(n) + j+2] +
c[(i+1) * sqrt(n) + j] + c[(i+1) * sqrt(n) + j+2] >= 16.2273);
}
}
//
}
//d(x) = 4 - x
if (max_distance == 3)
{
// Gridgraphen 1x2 (6 Knoten)
for (int i = 0; i < sqrt(n)-1; i+=1)
{
for (int j = 0; j < sqrt(n)-2; j+=2)
{
model.add(c[i * sqrt(n) + j] + c[i * sqrt(n) + j+2] +
c[(i+1) * sqrt(n) + j] + c[(i+1) * sqrt(n) + j+2] >= 30.283);
}
}
}
for (unsigned int v = 0; v < n; v++)
{
model.add(c[v] <= lambda);
model.add(c[v] >= 0);
}
std::cout << "Number of variables " << Info.numVar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
IloTimer timer(env);
timer.start();
int solveError = cplex.solve();
timer.stop();
if (!solveError)
{
std::cout << "STATUS : " << cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : " << cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
Info.numConstr = cplex.getNrows();
env.out() << " Number of constraints = " << Info.numConstr << "\n";
lambda_G_d = cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u = 0; u < n; u++)
{
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u] << " ";
}
} // end try
catch (IloException& e)
{
std::cerr << "Concert exception caught: " << e << std::endl;
} catch (...)
{
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
void kMST_ILP::modelSCF()
{
// single commodity flow model
// flow for each edge
flow_scf = IloNumVarArray(env, edges.getSize());
for (unsigned int i=0; i<flow_scf.getSize(); i++) {
flow_scf[i] = IloNumVar(env, Tools::indicesToString("flow", i).c_str());
// non-zero
model.add(0 <= flow_scf[i]);
// at most k, also ensures that edge is taken if flow is non-zero
model.add(flow_scf[i] <= k*edges[i]);
}
// 0 emits k+1 tokens
{
vector<u_int> outgoingEdgeIds;
getOutgoingEdgeIds(outgoingEdgeIds, 0);
IloExpr outgoingFlowSum(env);
for (unsigned int i=0; i<outgoingEdgeIds.size(); i++) {
outgoingFlowSum += flow_scf[ outgoingEdgeIds[i] ];
}
model.add(outgoingFlowSum == k);
}
// flow conservation, each node, which is taken, eats one (except first)
{
vector<u_int> outgoingEdgeIds;
getOutgoingEdgeIds(outgoingEdgeIds, 0);
for (unsigned int vertex=1; vertex<instance.n_nodes; vertex++) {
vector<u_int> outgoingEdgeIds, incomingEdgeIds;
getIncomingEdgeIds(incomingEdgeIds, vertex);
IloExpr incomingFlowSum(env);
IloExpr incomingEdgesSum(env);
for (unsigned int i=0; i<incomingEdgeIds.size(); i++) {
incomingFlowSum += flow_scf[ incomingEdgeIds[i] ];
incomingEdgesSum += edges[ incomingEdgeIds[i] ];
}
getOutgoingEdgeIds(outgoingEdgeIds, vertex);
IloExpr outgoingFlowSum(env);
for (unsigned int i=0; i<outgoingEdgeIds.size(); i++) {
outgoingFlowSum += flow_scf[ outgoingEdgeIds[i] ];
}
// vertex is part solution if one incoming vertex is used
model.add(incomingFlowSum - outgoingFlowSum == incomingEdgesSum);
incomingFlowSum.end();
incomingEdgesSum.end();
outgoingFlowSum.end();
}
}
}
int
main(int, char**)
{
IloEnv env;
try {
IloInt j;
define_data(env);
IloModel model(env);
IloNumVarArray m(env, nbElements, 0.0, IloInfinity);
IloNumVarArray r(env, nbRaw, 0.0, IloInfinity);
IloNumVarArray s(env, nbScrap, 0.0, IloInfinity);
IloNumVarArray i(env, nbIngot, 0, 100000, ILOINT);
IloNumVarArray e(env, nbElements);
// Objective Function: Minimize Cost
model.add(IloMinimize(env, IloScalProd(nm, m) + IloScalProd(nr, r) +
IloScalProd(ns, s) + IloScalProd(ni, i) ));
// Min and max quantity of each element in alloy
for (j = 0; j < nbElements; j++)
e[j] = IloNumVar(env, p[j] * alloy, P[j] * alloy);
// Constraint: produce requested quantity of alloy
model.add(IloSum(e) == alloy);
// Constraints: Satisfy element quantity requirements for alloy
for (j = 0; j < nbElements; j++) {
model.add(e[j] == m[j] + IloScalProd(PRaw[j], r)
+ IloScalProd(PScrap[j], s)
+ IloScalProd(PIngot[j], i));
}
// Optimize
IloCplex cplex(model);
cplex.setOut(env.getNullStream());
cplex.setWarning(env.getNullStream());
cplex.solve();
if (cplex.getStatus() == IloAlgorithm::Infeasible)
env.out() << "No Solution" << endl;
env.out() << "Solution status: " << cplex.getStatus() << endl;
// Print results
env.out() << "Cost:" << cplex.getObjValue() << endl;
env.out() << "Pure metal:" << endl;
for(j = 0; j < nbElements; j++)
env.out() << j << ") " << cplex.getValue(m[j]) << endl;
env.out() << "Raw material:" << endl;
for(j = 0; j < nbRaw; j++)
env.out() << j << ") " << cplex.getValue(r[j]) << endl;
env.out() << "Scrap:" << endl;
for(j = 0; j < nbScrap; j++)
env.out() << j << ") " << cplex.getValue(s[j]) << endl;
env.out() << "Ingots : " << endl;
for(j = 0; j < nbIngot; j++)
env.out() << j << ") " << cplex.getValue(i[j]) << endl;
env.out() << "Elements:" << endl;
for(j = 0; j < nbElements; j++)
env.out() << j << ") " << cplex.getValue(e[j]) << endl;
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
int CProblem::setModel() {
//time_t start, end;
numvar = 1+n; // lambda + all c;
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n);//
for (unsigned int u=0; u<n; u++) {
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u]=IloNumVar(env, str.c_str());
}
IloArray<IloIntVarArray> z(env,n);
for (unsigned int u=0; u<n; u++) {
z[u]= IloIntVarArray(env, n);
for (unsigned int v=0; v<n; v++) {
std::stringstream ss;
ss << u;
ss << v;
std::string str = "z" + ss.str();
z[u][v] = IloIntVar(env, 0, 1, str.c_str());
}
}
/* Constant M*/
int M=n*max_d;
UB = M;
/* Objective*/
model.add(IloMinimize(env, lambda));
/*Constrains*/
model.add(lambda - UB <= 0);
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env,n);
for (unsigned int u=0; u<n; u++) {
Par_d[u]=IloNumArray(env,n);
for (unsigned int v=0; v<n; v++) {
Par_d[u][v]=d[u][v];
}
}
for (unsigned u=0; u<n; u++) {
for (unsigned v=0; v<u; v++) {
model.add(c[v]-c[u]+M* z[u][v] >= Par_d[u][v] );
model.add(c[u]-c[v]+M*(1-z[u][v]) >= Par_d[u][v]);
numvar++; // + z[u][v]
}
}
for (unsigned int v=0; v<n; v++) {
IloExpr expr;
model.add (c[v] <= lambda);
model.add (c[v] >= 0);
expr.end();
}
std::cout << "Number of variables " << numvar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
/*
start = clock();
int solveError = cplex.solve();
end = clock ();
*/
// cplex.setParam(IloCplex::Threads, 1);
cplex.setParam(IloCplex::ClockType , 1 ); // CPU time
IloTimer timer(env);
const double startt = cplex.getTime();
timer.start();
int solveError = cplex.solve();
timer.stop();
const double stopt = cplex.getTime() - startt;
if ( !solveError ) {
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
env.out() << "Time cplex.getTime " << stopt << "\n";
numconst = cplex.getNrows();
env.out() << " Number of constraints = " << numconst << "\n";
lambda_G_d=cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u=0; u<n; u++) {
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u]<< " ";
}
std::cout <<"\n";
/*
for (unsigned int u=0; u<n; u++) {
for (unsigned int v=0; v<u; v++) {
std::cout<< "z[" << u <<"][" << v << "]="<< cplex.getValue( z[u][v]) << " ";
Z.push_back(cplex.getValue( z[u][v]));
}
std::cout << "\n";
}
std::cout <<"\n";
*/
} // end try
catch (IloException& e) {
std::cerr << "Concert exception caught: " << e << std::endl;
}
catch (...) {
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
int main (int argc, char *argv[])
{
ifstream infile;
infile.open("Proj3_op.txt");
if(!infile){
cerr << "Unable to open the file\n";
exit(1);
}
cout << "Before Everything!!!" << "\n";
IloEnv env;
IloInt i,j,varCount1,varCount2,varCount3,conCount; //same as “int i;”
IloInt k,w,K,W,E,l,P,N,L;
IloInt tab, newline, val; //from file
char line[2048];
try {
N = 9;
K = 3;
L = 36;
W = (IloInt)atoi(argv[1]);
IloModel model(env); //set up a model object
IloNumVarArray var1(env);// C - primary
cout << "Here\n";
IloNumVarArray var2(env);// B - backup
cout << "here1\n";
IloNumVarArray var3(env);// = IloNumVarArray(env,W); //declare an array of variable objects, for unknowns
IloNumVar W_max(env, 0, W, ILOINT);
//var1: c_ijk_w
IloRangeArray con(env);// = IloRangeArray(env,N*N + 3*w); //declare an array of constraint objects
IloNumArray2 t = IloNumArray2(env,N); //Traffic Demand
IloNumArray2 e = IloNumArray2(env,N); //edge matrix
//IloObjective obj;
//Define the Xijk matrix
cout << "Before init xijkl\n";
Xijkl xijkl_m(env, L);
for(l=0;l<L;l++){
xijkl_m[l] = Xijk(env, N);
for(i=0;i<N;i++){
xijkl_m[l][i] = Xjk(env, N);
for(j=0;j<N;j++){
xijkl_m[l][i][j] = IloNumArray(env, K);
}
}
}
//reset everything to zero here
for(l=0;l<L;l++)
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
xijkl_m[l][i][j][k] = 0;
input_Xijkl(xijkl_m);
cout<<"bahre\n";
for(i=0;i<N;i++){
t[i] = IloNumArray(env,N);
for(j=0;j<N;j++){
if(i == j)
t[i][j] = IloNum(0);
else if(i != j)
t[i][j] = IloNum((i+j+2)%5);
}
}
printf("ikde\n");
//Minimize W_max
IloObjective obj=IloMinimize(env);
obj.setLinearCoef(W_max, 1.0);
cout << "here khali\n";
//Setting var1[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var1.add(IloNumVar(env, 0, 1, ILOINT));
//c_ijk_w variables set.
//Setting var2[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var2.add(IloNumVar(env, 0, 1, ILOINT));
//b_ijk_w variables set.
for(w = 0;w < W;w++)
var3.add(IloNumVar(env, 0, 1, ILOINT)); //Variables for u_w
cout<<"variables ready\n";
conCount = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++){
con.add(IloRange(env, 2 * t[i][j], 2 * t[i][j]));
//varCount1 = 0;
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],1.0);
}
conCount++;
}//Adding Demands Constraints to con
cout<<"1st\n";
IloInt z= 0;
for(w=0;w<W;w++){
for(l=0;l<L;l++){
con.add(IloRange(env, -IloInfinity, 1));
for(i=0;i<N;i++){
for(j=0;j<N;j++){
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
}
}
}
conCount++;
}
}
cout<<"2nd\n";
//Adding Wavelength Constraints_1 to con
P = N * (N-1) * K;
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 0));
varCount1 = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
con[conCount].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w],1.0);
}
con[conCount].setLinearCoef(var3[w],-P);
conCount++;
}
cout<<"3rd\n";
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 1));
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w], 1.0);
con[conCount++].setLinearCoef(var2[i*N*W*K+j*W*K+k*W+w], 1.0);
}
varCount3 = 0;
for(w=0;w<W;w++){
con.add(IloRange(env, 0, IloInfinity));
con[conCount].setLinearCoef(W_max, 1.0);
con[conCount++].setLinearCoef(var3[w], -1.0 * (w+1));
}
cout<<"after constraints\n";
//model.add(obj); //add objective function into model
model.add(IloMinimize(env,obj));
model.add(con); //add constraints into model
IloCplex cplex(model); //create a cplex object and extract the //model to this cplex object
// Optimize the problem and obtain solution.
if ( !cplex.solve() ) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
IloNumArray vals(env); //declare an array to store the outputs
//if 2 dimensional: IloNumArray2 vals(env);
env.out() << "Solution status = " << cplex.getStatus() << endl;
//return the status: Feasible/Optimal/Infeasible/Unbounded/Error/…
env.out() << "Solution value = " << cplex.getObjValue() << endl;
//return the optimal value for objective function
cplex.getValues(vals, var1); //get the variable outputs
env.out() << "Values Var1 = " << vals << endl; //env.out() : output stream
cplex.getValues(vals, var3);
env.out() << "Values Val3 = " << vals << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end(); //close the CPLEX environment
return 0;
} // END main
MILPSolverCPX::MILPSolverCPX(MILPSolverCPX & c)
{
static const bool debug = false;
if (debug) {
cout << "Copying...\n";
ostringstream copyfn;
copyfn << "copyconstructor" << *(c.envUsers) << ".lp";
const string copyfns = copyfn.str();
c.writeLp(copyfns.c_str());
}
env = c.env;
envUsers = c.envUsers;
++(*envUsers);
model = new IloModel(*env);
modelvar = new IloNumVarArray(*env);
modelcon = new Constraints();
obj = new IloObjective(*env);
obj->setSense(IloObjective::Minimize);
model->add(*obj);
colCount = c.colCount;
rowCount = c.rowCount;
integervars = c.integervars;
map<int,bool>::const_iterator iItr = integervars.begin();
const map<int,bool>::const_iterator iEnd = integervars.end();
for (int ci = 0; ci < colCount; ++ci) {
const double lb = (*(c.modelvar))[ci].getLB();
const double ub = (*(c.modelvar))[ci].getUB();
if (iItr != iEnd && iItr->first == ci) {
modelvar->add(IloNumVar(*env, lb, ub, (iItr->second ? ILOBOOL : ILOINT)));
//cout << "Column " << ci << " in the copy is an integer\n";
++iItr;
} else {
modelvar->add(IloNumVar(*env, lb, ub));
}
const char * oldName = (*(c.modelvar))[ci].getName();
(*modelvar)[ci].setName(oldName);
}
coeffs = c.coeffs;
map<int,map<int,double> >::const_iterator coItr = coeffs.begin();
const map<int,map<int,double> >::const_iterator coEnd = coeffs.end();
map<int,IloRange>::iterator dItr = c.modelcon->data.begin();
const map<int,IloRange>::iterator dEnd = c.modelcon->data.end();
for (int r = 0; dItr != dEnd; ++r, ++dItr) {
const double lb = dItr->second.getLB();
const double ub = dItr->second.getUB();
IloRange & newRange = modelcon->data[r] = IloAdd(*model, IloRange(*env, lb,ub));
if (dItr->second.getName()) {
newRange.setName(dItr->second.getName());
}
if (coItr == coEnd) continue;
if (r < coItr->first) continue;
map<int,double>::const_iterator fItr = coItr->second.begin();
const map<int,double>::const_iterator fEnd = coItr->second.end();
for (; fItr != fEnd; ++fItr) {
newRange.setLinearCoef((*modelvar)[fItr->first], fItr->second);
}
++coItr;
}
cplex = new IloCplex(*model);
setILS(cplex);
solArray = 0;
if (debug) {
ostringstream copyfn;
copyfn << "aftercopyconstructor" << *(c.envUsers) << ".lp";
const string copyfns = copyfn.str();
writeLp(copyfns.c_str());
}
}
int CProblem::setModel() {
//time_t start, end;
numvar = 1+n; // lambda + all c;
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n);//
for (unsigned int u=0; u<n; u++) {
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u]=IloNumVar(env, str.c_str());
}
IloArray<IloIntVarArray> z(env,n);
for (unsigned int u=0; u<n; u++) {
z[u]= IloIntVarArray(env, n);
for (unsigned int v=0; v<n; v++) {
std::stringstream ss;
ss << u;
ss << v;
std::string str = "z" + ss.str();
z[u][v] = IloIntVar(env, 0, 1, str.c_str());
}
}
/* Constant M*/
int UB, LB;
switch (lattice){
case 1: // hexagonal_lattice
if (max_d==3) {LB = 5; UB = 10;} //d(x) = 3 -x
else {LB = 9; UB = 27;}
break;
case 2:// triangular_lattice
if (max_d==3) { LB = 8; UB = 16;} //d(x) = 3 -x
else {LB = 18; UB = 54;}
break;
case 3:// square_lattice
if (max_d==3) { LB = 6; UB = 12;} //d(x) = 3 -x
else { LB = 11; UB = 33;}
break;
default: UB=n*max_d;break;
}
std::cout << "Lattice " << lattice << "\n";
std::cout << "UB for lambda " << UB << "\n";
/* Objective*/
model.add(IloMinimize(env, lambda));
/*Constrains*/
model.add(lambda - UB <= 0);
model.add(lambda - LB >= 0);
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env,n);
for (unsigned int u=0; u<n; u++) {
Par_d[u]=IloNumArray(env,n);
for (unsigned int v=0; v<n; v++) {
Par_d[u][v]=d[u][v];
}
}
int M = INT_MAX;//Par_d[0][0] + UB;
for (unsigned u=0; u<n; u++) {
for (unsigned v=0; v<u; v++) {
model.add(c[v]-c[u]+M* z[u][v] >= Par_d[u][v] );
model.add(c[u]-c[v]+M*(1-z[u][v]) >= Par_d[u][v]);
numvar++; // + z[u][v]
}
}
for (unsigned int v=0; v<n; v++) {
IloExpr expr;
model.add (c[v] <= lambda);
model.add (c[v] >= 0);
expr.end();
}
std::cout << "Number of variables " << numvar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
IloTimer timer(env);
timer.start();
int solveError = cplex.solve();
timer.stop();
if ( !solveError ) {
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
numconst = cplex.getNrows();
env.out() << " Number of constraints = " << numconst << "\n";
lambda_G_d=cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u=0; u<n; u++) {
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u]<< " ";
}
std::cout <<"\n";
/*
for (unsigned int u=0; u<n; u++) {
for (unsigned int v=0; v<u; v++) {
std::cout<< "z[" << u <<"][" << v << "]="<< cplex.getValue( z[u][v]) << " ";
Z.push_back(cplex.getValue( z[u][v]));
}
std::cout << "\n";
}
std::cout <<"\n";
*/
} // end try
catch (IloException& e) {
std::cerr << "Concert exception caught: " << e << std::endl;
}
catch (...) {
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
int main (int argc, char *argv[])
{
ifstream infile;
clock_t start_time, end_time;
infile.open("Proj3_op.txt");
if(!infile){
cerr << "Unable to open the file\n";
exit(1);
}
cout << "Before Everything!!!" << "\n";
IloEnv env;
IloInt i,j,varCount1,varCount2,varCount3,conCount; //same as “int i;”
IloInt k,w,K,W,E,l,P,N,L;
IloInt tab, newline, val; //from file
char line[2048];
try {
N = 9;
K = 2;
L = 36;
W = (IloInt)atoi(argv[1]);
IloModel model(env); //set up a model object
IloNumVarArray var1(env);// = IloNumVarArray(env,K*W*N*N);
IloNumVarArray var3(env);// = IloNumVarArray(env,W); //declare an array of variable objects, for unknowns
IloNumVar W_max(env, 0, W, ILOINT);
IloRangeArray con(env);// = IloRangeArray(env,N*N + 3*w); //declare an array of constraint objects
IloNumArray2 t = IloNumArray2(env,N); //Traffic Demand
IloNumArray2 e = IloNumArray2(env,N); //edge matrix
//IloObjective obj;
//Define the Xijk matrix
Xijkl xijkl_m(env, L);
for(l=0;l<L;l++){
xijkl_m[l] = Xijk(env, N);
for(i=0;i<N;i++){
xijkl_m[l][i] = Xjk(env, N);
for(j=0;j<N;j++){
xijkl_m[l][i][j] = IloNumArray(env, K);
}
}
}
//reset everything to zero here
for(l=0;l<L;l++)
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
xijkl_m[l][i][j][k] = 0;
input_Xijkl(xijkl_m);
cout<<"bahre\n";
FILE *file;
file = fopen(argv[2],"r");
int tj[10];
for(i=0;i<N;i++){
t[i] = IloNumArray(env,N);
fscanf(file,"%d %d %d %d %d %d %d %d %d \n",&tj[0],&tj[1],&tj[2],&tj[3],&tj[4],&tj[5],&tj[6],&tj[7],&tj[8]);
for(j=0;j<N;j++){
t[i][j] = IloNum(tj[j]);
}
}
printf("ikde\n");
//Minimize W_max
IloObjective obj=IloMinimize(env);
obj.setLinearCoef(W_max, 1.0);
cout << "here khali\n";
//Setting var1[] for Demands Constraints
for(i=0;i<N;i++)
for(j=0;j<N;j++)
for(k=0;k<K;k++)
for(w=0;w<W;w++)
var1.add(IloNumVar(env, 0, 1, ILOINT));
for(w = 0;w < W;w++)
var3.add(IloNumVar(env, 0, 1, ILOINT)); //Variables for u_w
cout<<"variables ready\n";
conCount = 0;
for(i=0;i<N;i++)
for(j=0;j<N;j++){
con.add(IloRange(env, t[i][j], t[i][j]));
//varCount1 = 0;
for(k=0;k<K;k++)
for(w=0;w<W;w++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
//cout << "Before Adding Constraint\n";
//con[1].setLinearCoef(IloNumVar(env, 0, 1, ILOINT), 1.0);
//cout<<"coef set "<<varCount1;
}
conCount++;
}//Adding Demands Constraints to con
cout<<"1st\n";
IloInt z= 0;
for(w=0;w<W;w++){
for(l=0;l<L;l++){
con.add(IloRange(env, -IloInfinity, 1));
for(i=0;i<N;i++){
for(j=0;j<N;j++){
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],xijkl_m[l][i][j][k]);
}
}
}
conCount++;
}
}
cout<<"2nd\n";
//Adding Wavelength Constraints_1 to con
P = N * (N-1) * K;
for(w=0;w<W;w++){
con.add(IloRange(env, -IloInfinity, 0));
varCount1 = 0;
for(i=0;i<9;i++)
for(j=0;j<9;j++)
for(k=0;k<K;k++){
con[conCount].setLinearCoef(var1[i*N*W*K+j*W*K+k*W+w],1.0);
}
con[conCount].setLinearCoef(var3[w],-P);
conCount++;
}
cout<<"3rd\n";
varCount3 = 0;
for(w=0;w<W;w++){
con.add(IloRange(env, 0, IloInfinity));
con[conCount].setLinearCoef(W_max, 1.0);
con[conCount++].setLinearCoef(var3[w], -1.0 * (w+1));
}
cout<<"after constraints\n";
//model.add(obj); //add objective function into model
model.add(IloMinimize(env,obj));
model.add(con); //add constraints into model
IloCplex cplex(model); //create a cplex object and extract the //model to this cplex object
// Optimize the problem and obtain solution.
start_time = clock();
if ( !cplex.solve() ) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
end_time = clock();
IloNumArray vals(env); //declare an array to store the outputs
IloNumVarArray opvars(env); //if 2 dimensional: IloNumArray2 vals(env);
//env.out() << "Solution status = " << cplex.getStatus() << endl;
//return the status: Feasible/Optimal/Infeasible/Unbounded/Error/…
env.out() << "W_max value = " << cplex.getObjValue() << endl;
//return the optimal value for objective function
cplex.getValues(vals, var1); //get the variable outputs
//env.out() << "Values Var1 = " << vals << endl; //env.out() : output stream
cplex.getValues(vals, var3);
//env.out() << "Values Val3 = " << vals << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end(); //close the CPLEX environment
float running_time (((float)end_time - (float)start_time)/CLOCKS_PER_SEC);
cout << "*******RUNNING TIME: " << running_time << endl;
return 0;
} // END main
int
main(int argc)
{
IloEnv env;
try {
IloModel model(env);
NumVarMatrix varOutput(env, J + current);
NumVar3Matrix varHelp(env, J + current);
Range3Matrix cons(env, J + current);
for (int j = 0; j <J + current; j++){
varOutput[j] = IloNumVarArray(env, K);
varHelp[j] = NumVarMatrix(env, K);
cons[j] = RangeMatrix(env, K);
for (int k = 0; k < K; k++){
varOutput[j][k] = IloNumVar(env, 0.0, IloInfinity);
varHelp[j][k] = IloNumVarArray(env, L);
cons[j][k] = IloRangeArray(env, C);
for (int l = 0; l < L; l++){
varHelp[j][k][l] = IloNumVar(env, 0.0, IloInfinity);
}
if (j > current){
cons[j][k][0] = IloRange(env, 0.0, 0.0);//will be used to express equality of varOutput, constraint (0)
cons[j][k][1] = IloRange(env, 0.0, IloInfinity);// constraint (1)
cons[j][k][2] = IloRange(env, -IloInfinity, T[j] - Tdc - Tblow[j] - Tslack);// constraint (2)
cons[j][k][3] = IloRange(env, Tfd[k], Tfd[k]);// constraint (3)
cons[j][k][4] = IloRange(env, 0.0, IloInfinity);// constraint (4)
cons[j][k][5] = IloRange(env, Tdf[k], IloInfinity);// constraint (5)
cons[j][k][6] = IloRange(env, T[j - a[k]] + Tcd, T[j - a[k]] + Tcd);// constraint (6)
cons[j][k][7] = IloRange(env, TlossD[k], IloInfinity);// constraint (7)
cons[j][k][8] = IloRange(env, TlossF[k], IloInfinity);// constraint (8)
}
}
}
populatebynonzero(model, varOutput, varHelp, cons);
IloCplex cplex(model);
// Optimize the problem and obtain solution.
if (!cplex.solve()) {
env.error() << "Failed to optimize LP" << endl;
throw(-1);
}
IloNumArray vals(env);
IloNumVar val(env);
//vars to save output
double TimeAvailable[J][K];
double TimeInstances[J][K][L];
double LK103[J][2];
env.out() << "Solution status = " << cplex.getStatus() << endl;
env.out() << "Solution value = " << cplex.getObjValue() << endl;
for (int j = current; j < current + J; ++j)
{
cplex.getValues(vals, varOutput[j]);
env.out() << "Seconds for load "<<j<<" = " << vals << endl;
/*for (int k = 0; k < K; k++){
TimeAvailable[j][k] = cplex.getValue(varOutput[j][k]);
}*/
}
for (int j = current; j < current + J; j++){
for (int k = 0; k < K; k++){
cplex.getValues(vals, varHelp[j][k]);
env.out() << "Time instances for spoon "<<k<<" in load "<<j<<" = " << vals << endl;
/*for (int l = 0; l < L; l++){
TimeInstances[j][k][l] = cplex.getValue(varHelp[j][k][l]);
}*/
}
}
for (int j = current + 2; j < J + current; j++){
LK103[j][0] = TimeInstances[j - 2][0][0];
LK103[j][1] = TimeInstances[j][0][5];
env.out() << "LK103, load " << j << " : " << LK103[j][1]-LK103[j][0] << endl;
}
/*cplex.getSlacks(vals, cons);
env.out() << "Slacks = " << vals << endl;
cplex.getDuals(vals, cons);
env.out() << "Duals = " << vals << endl;
cplex.getReducedCosts(vals, varOutput);
env.out() << "Reduced Costs = " << vals << endl;*/
cplex.exportModel("lpex1.lp");
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end();
cin.get();
return 0;
} // END main
int
main(int argc, char **argv)
{
IloEnv env;
try {
IloInt i, j;
IloNum rollWidth;
IloNumArray amount(env);
IloNumArray size(env);
if ( argc > 1 )
readData(argv[1], rollWidth, size, amount);
else
readData("../../../examples/data/cutstock.dat",
rollWidth, size, amount);
/// CUTTING-OPTIMIZATION PROBLEM ///
IloModel cutOpt (env);
IloObjective RollsUsed = IloAdd(cutOpt, IloMinimize(env));
IloRangeArray Fill = IloAdd(cutOpt,
IloRangeArray(env, amount, IloInfinity));
IloNumVarArray Cut(env);
IloInt nWdth = size.getSize();
for (j = 0; j < nWdth; j++) {
Cut.add(IloNumVar(RollsUsed(1) + Fill[j](int(rollWidth / size[j]))));
}
IloCplex cutSolver(cutOpt);
/// PATTERN-GENERATION PROBLEM ///
IloModel patGen (env);
IloObjective ReducedCost = IloAdd(patGen, IloMinimize(env, 1));
IloNumVarArray Use(env, nWdth, 0.0, IloInfinity, ILOINT);
patGen.add(IloScalProd(size, Use) <= rollWidth);
IloCplex patSolver(patGen);
/// COLUMN-GENERATION PROCEDURE ///
IloNumArray price(env, nWdth);
IloNumArray newPatt(env, nWdth);
/// COLUMN-GENERATION PROCEDURE ///
for (;;) {
/// OPTIMIZE OVER CURRENT PATTERNS ///
cutSolver.solve();
report1 (cutSolver, Cut, Fill);
/// FIND AND ADD A NEW PATTERN ///
for (i = 0; i < nWdth; i++) {
price[i] = -cutSolver.getDual(Fill[i]);
}
ReducedCost.setLinearCoefs(Use, price);
patSolver.solve();
report2 (patSolver, Use, ReducedCost);
if (patSolver.getValue(ReducedCost) > -RC_EPS) break;
patSolver.getValues(newPatt, Use);
Cut.add( IloNumVar(RollsUsed(1) + Fill(newPatt)) );
}
cutOpt.add(IloConversion(env, Cut, ILOINT));
cutSolver.solve();
cout << "Solution status: " << cutSolver.getStatus() << endl;
report3 (cutSolver, Cut);
}
catch (IloException& ex) {
cerr << "Error: " << ex << endl;
}
catch (...) {
cerr << "Error" << endl;
}
env.end();
return 0;
}
void AssetAllocationModel::BuildCplexModel(IloEnv &env, IloModel &model, IloExpr &objective, unsigned int stage, const double *prev_decisions, const double *scenario, vector<IloNumVar> &decision_vars, vector<IloRange> &dual_constraints) const {
//assets count N
unsigned int assets = GetAssetsCount();
bool root_node = (stage == 1);
bool last_stage = (stage == GetStagesCount());
double conf_inv = 1 - param_.confidence;
double conf_inv_other = 1 - param_.confidence_other;
IloNumVarArray x(env, assets);
for (unsigned int i = 0; i < assets; ++i) {
//zero lower bound
x[i] = IloNumVar(env, 0.0);
//max profit = min negative loss
if (!root_node) {
switch (param_.risk_measure) {
case RISK_CVAR_NESTED:
objective += -1 * x[i];
break;
case RISK_CVAR_MULTIPERIOD:
objective += -1 * param_.expectation_coefficients[stage - 1] * x[i];
break;
}
}
decision_vars.push_back(x[i]);
}
model.add(x);
//var c = positive value in the cvar
IloNumVar c(env, 0.0);
if (!root_node && (param_.risk_measure == RISK_CVAR_MULTIPERIOD)) {
//objective with risk aversion coef lambda
objective += param_.risk_coefficients[stage - 1] / conf_inv * c;
model.add(c);
}
//var c_other = positive value in the cvar
IloNumVar c_other(env, 0.0);
if (!root_node && (param_.risk_measure == RISK_CVAR_MULTIPERIOD)) {
//objective with risk aversion coef lambda
objective += param_.risk_coefficients_other[stage - 1] / conf_inv_other * c_other;
model.add(c_other);
}
//var var = variable to calculate CVaR = VaR level
IloNumVar var(env, GetRecourseLowerBound(stage), GetRecourseUpperBound(stage));
//objective according to the risk aversion lambda
if (!last_stage) {
//last stage has no recourse
objective += param_.risk_coefficients[stage] * var;
}
model.add(var);
decision_vars.push_back(var);
//var var_other = variable to calculate CVaR = VaR level
IloNumVar var_other(env, GetRecourseLowerBound(stage), GetRecourseUpperBound(stage));
//objective according to the risk aversion lambda
if (!last_stage) {
//last stage has no recourse
objective += param_.risk_coefficients_other[stage] * var_other;
}
model.add(var_other);
decision_vars.push_back(var_other);
//transaction costs dummy variable
IloNumVarArray trans(env, assets);
for (unsigned int i = 0; i < assets; ++i) {
//zero lower bound
trans[i] = IloNumVar(env, 0.0);
}
if (!root_node) {
model.add(trans);
}
//constraint capital = sum of the weights equals initial capital one
IloExpr cap_expr(env);
double transaction_coef = param_.transaction_costs;
for (unsigned int i = 0; i < assets; ++i) {
cap_expr += x[i];
if (!root_node) {
cap_expr += transaction_coef * trans[i];
}
}
double total_capital;
if (root_node) {
//no parent, init the capital with 1
total_capital = 1.0;
}
else {
//sum up the capital under this scenario
total_capital = CalculateCapital(prev_decisions, scenario);
}
IloRange capital(env, total_capital, cap_expr, total_capital);
model.add(capital);
dual_constraints.push_back(capital);
//constraint transaction costs
IloRangeArray costs_pos(env);
IloRangeArray costs_neg(env);
if (!root_node) {
for (unsigned int i = 0; i < assets; ++i) {
//current position in asset i
double parent_value = scenario[i] * prev_decisions[i]; //decisions and scenarios have the same order here, decisions start with allocations
//positive and negative part of linearization for absolute value
IloExpr cost_expr_pos(env);
IloExpr cost_expr_neg(env);
cost_expr_pos += x[i];
cost_expr_pos += -1 * trans[i];
cost_expr_neg += -1 * x[i];
cost_expr_neg += -1 * trans[i];
costs_pos.add(IloRange(env, cost_expr_pos, parent_value));
costs_neg.add(IloRange(env, cost_expr_neg, -parent_value));
dual_constraints.push_back(costs_pos[i]);
dual_constraints.push_back(costs_neg[i]);
}
model.add(costs_pos);
model.add(costs_neg);
}
//contraint the positive part "c" of the CVaR value
IloExpr cvar_expr(env);
IloRange cvar;
if (!root_node && (param_.risk_measure == RISK_CVAR_MULTIPERIOD)) {
cvar_expr += c; //the varible itself
for (unsigned int i = 0; i < assets; ++i) {
cvar_expr += x[i]; //-c transp x
}
double parent_var = prev_decisions[assets]; //var is right after the allocations in decision vector
cvar = IloRange(env, -parent_var, cvar_expr); //previous value at risk
model.add(cvar);
dual_constraints.push_back(cvar);
}
//contraint the positive part "c_other" of the CVaR value
IloExpr cvar_expr_other(env);
IloRange cvar_other;
if (!root_node && (param_.risk_measure == RISK_CVAR_MULTIPERIOD)) {
cvar_expr_other += c_other; //the varible itself
for (unsigned int i = 0; i < assets; ++i) {
cvar_expr_other += x[i]; //-c transp x
}
double parent_var_other = prev_decisions[assets + 1]; //var_other is right after var in the decisions vector
cvar_other = IloRange(env, -parent_var_other, cvar_expr_other); //previous value at risk
model.add(cvar_other);
dual_constraints.push_back(cvar_other);
}
}
static void populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c) {
IloEnv env = model.getEnv();
IloNumArray costs(env);
IloNumArray time(env);
IloNumArray product(env);
int costs_array[] = {1,1,1,10,1,12,2,2,5,10};
int time_array[] = {10,1,7,3,2,3,2,3,7,1};
int product_array[] = {0,3,1,2,-2,0,0,0,0,0};
for(int i=0;i<10;i++)
costs.add(costs_array[i]);
for(int i=0;i<10;i++)
time.add(time_array[i]);
for(int i=0;i<10;i++)
product.add(product_array[i]);
x.add(IloBoolVar(env,"x12")); //0
x.add(IloBoolVar(env,"x24")); //1
x.add(IloBoolVar(env,"x46")); //2
x.add(IloBoolVar(env,"x13")); //3
x.add(IloBoolVar(env,"x32")); //4
x.add(IloBoolVar(env,"x35")); //5
x.add(IloBoolVar(env,"x56")); //6
x.add(IloBoolVar(env,"x25")); //7
x.add(IloBoolVar(env,"x34")); //8
x.add(IloBoolVar(env,"x45")); //9
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s2")); //10
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s3")); //11
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s4")); //12
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s5")); //13
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s1")); //14
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"s6")); //15
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q2")); //16
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q3")); //17
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q4")); //18
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q5")); //19
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q1")); //20
x.add(IloNumVar(env,0,IloInfinity,ILOINT,"q6")); //21
model.add(IloMinimize(env, costs[0]*x[0] + costs[1]*x[1] + costs[2]*x[2] + costs[3]*x[3] + costs[4]*x[4] + costs[5]*x[5] + costs[6]*x[6] + costs[7]*x[7] + costs[8]*x[8] + costs[9]*x[9]));
c.add(x[0]+ x[3] == 1); // arcs sortant du noeud de depart
c.add(x[2]+ x[6] == 1); // arcs entrant au noeud d arrivee
c.add(x[1]+ x[7] - x[0] - x[4] == 0);
c.add(x[8]+ x[5] + x[4] - x[3] == 0);
c.add(x[9]+ x[2] - x[1] - x[8] == 0);
c.add(x[6]- x[7] - x[5] - x[9] == 0);
c.add(time[0]*x[0] + time[1]*x[1] + time[2]*x[2] + time[3]*x[3] + time[4]*x[4] + time[5]*x[5] + time[6]*x[6] + time[7]*x[7] + time[8]*x[8] + time[9]*x[9] <= 14);
//c.add(product[0]*x[0] + product[1]*x[1] + product[2]*x[2] + product[3]*x[3] + product[4]*x[4] + product[5]*x[5] + product[6]*x[6] + product[7]*x[7] + product[8]*x[8] + product[9]*x[9] <= 4);
c.add(x[14]+time[0]-1000*(1-x[0]) - x[10]<= 0);
c.add(x[20]+product[0]-1000*(1-x[0]) - x[16]<= 0);
c.add(x[10]+time[1]-1000*(1-x[1]) - x[12]<= 0);
c.add(x[16]+product[1]-1000*(1-x[1]) - x[18]<= 0);
c.add(x[18]-product[1]-1000*(1-x[1]) - x[16]<= 0);
c.add(x[12]+time[2]-1000*(1-x[2]) - x[15]<= 0);
c.add(x[18]+product[2]-1000*(1-x[2]) - x[21]<= 0);
c.add(x[21]-product[2]-1000*(1-x[2]) - x[18]<= 0);
c.add(x[14]+time[3]-1000*(1-x[3]) - x[11]<= 0);
c.add(x[20]+product[3]-1000*(1-x[3]) - x[17]<= 0);
c.add(x[17]-product[3]-1000*(1-x[3]) - x[20]<= 0);
c.add(x[13]+time[6]-1000*(1-x[6]) - x[15]<= 0);
c.add(x[19]+product[6]-1000*(1-x[6]) - x[21]<= 0);
c.add(x[10]+time[7]-1000*(1-x[7]) - x[13]<= 0);
c.add(x[16]+product[7]-1000*(1-x[7]) - x[19]<= 0);
c.add(x[12]+time[9]-1000*(1-x[9]) - x[13]<= 0);
c.add(x[18]+product[9]-1000*(1-x[9]) - x[19]<= 0);
c.add(x[11]+time[4]-1000*(1-x[4]) - x[10] <=0);
c.add(x[17]+product[4]-1000*(1-x[4]) - x[16]<= 0);
c.add(x[16]-product[4]-1000*(1-x[4]) - x[17]<= 0);
c.add(x[11]+time[8]-1000*(1-x[8]) - x[12]<= 0);
c.add(x[17]+product[8]-1000*(1-x[8]) - x[18]<= 0);
c.add(x[11]+time[5]-1000*(1-x[5]) - x[13]<= 0);
c.add(x[17]+product[5]-1000*(1-x[5]) - x[19]<= 0);
c.add(5 <= x[10] <= 7);
c.add(2 <= x[11] <= 5);
c.add(5 <= x[12] <= 9);
c.add(0 <= x[13] <= 20);
c.add(0 <= x[14] <= 0);
c.add(0 <= x[15] <= 14);
/*
c.add(2 <= x[17] <= 4);
c.add(0 <= x[16] <= 2);
c.add(3 <= x[18] <= 4);
c.add(0 <= x[19] <= 1000);
//c.add(0 <= x[20] <= 0);*/
c.add(3 <= x[21] <= 4);
c.add( x[20] == 1);
model.add(c);
}
ILOSTLBEGIN
vector<double> solve_qp_cplex(QP* qp, bool& success)
{
vector<double> res(qp->num_var, 0);
IloEnv env;
try {
IloModel model(env);
// variables
IloNumVarArray var(env);
for (int i=0; i<qp->num_var; i++)
{
if (qp->binary_idx[i]==0)
var.add(IloNumVar(env, qp->lb[i], qp->ub[i]));
else
var.add(IloNumVar(env, qp->lb[i], qp->ub[i], ILOINT));
}
// constraints
IloRangeArray con(env);
//TODO remove these fake constraints
for (int i=0; i<qp->num_var; i++)
{
con.add( var[i]<= 1e10);
}
// iterate over constraint matrix
for (int i=0; i<qp->b.size(); i++)
{
vector<int>* idx = NULL;
vector<float>* coef = NULL;
int ret = qp->A.get(&idx, &coef, i);
assert(idx!=NULL);
assert(coef!=NULL);
assert(ret>0);
assert(idx->size()==coef->size());
IloNumExpr c_expr(env);
for (int j=0; j<idx->size(); j++)
{
//printf("j:%i %.3f %i (num_var: %i)\n", j, coef->at(j), idx->at(j), qp->num_var);
assert(idx->at(j)<qp->num_var);
c_expr+= coef->at(j)*var[idx->at(j)];
}
if (qp->eq_idx[i]>0)
{
con.add(c_expr == qp->b[i]);
//env.out() << i << c_expr << "==" << qp->b[i] << endl;
}
else
{
con.add(c_expr <= qp->b[i]);
//env.out() << i << c_expr << "<=" << qp->b[i] << endl;
}
}
// objective
IloNumExpr obj_expr(env);
qp->Q.reset_it();
while (true)
{
int i=0;
int j=0;
float val = qp->Q.next(&i, &j);
//printf("%i %i %.2f\n", i, j, val);
if (i==-1)
break;
assert(i<qp->num_var && j<qp->num_var);
obj_expr+= var[i] * var[j] * val;
}
for (int i=0; i<qp->F.size(); i++)
{
if (qp->F[i]!=0)
obj_expr+= var[i] * qp->F[i];
}
IloObjective obj(env, IloMinimize(env, obj_expr));
//model.add(IloMinimize(env, var[0] + 2 * var[1] + 3 * var[2] + var[3]));
//env.out() << "objective: " << IloMinimize(env, obj_expr) << endl;
//env.out() << "constraints: " << con << endl;
// create model
//model.add(var);
model.add(con);
model.add(obj);
// solve...
IloCplex cplex(model);
cplex.solve();
IloNumArray vals(env);
cplex.getValues(vals, var);
//env.out() << "Values = " << vals << endl;
//cplex.getSlacks(vals, con);
//env.out() << "Slacks = " << vals << endl;
env.out() << "Solution status = " << cplex.getStatus() << endl;
env.out() << "Solution value = " << cplex.getObjValue() << endl;
for (int i=0; i<qp->num_var; i++)
res[i] = vals[i];
}
catch (IloException& e)
{
cerr << "Concert exception caught: " << e << endl;
success = false;
}
catch (...)
{
cerr << "Unknown exception caught" << endl;
success = false;
}
env.end();
return res;
}
int CProblem::setModel() {
//time_t start, end;
numvar = 1+n; // lambda + all c;
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
/*Variables*/
IloNumVar lambda(env, "lambda");
IloNumVarArray c(env, n);//
for (unsigned int u=0; u<n; u++) {
std::stringstream ss;
ss << u;
std::string str = "c" + ss.str();
c[u]=IloNumVar(env, str.c_str());
}
IloArray<IloIntVarArray> z(env,n);
for (unsigned int u=0; u<n; u++) {
z[u]= IloIntVarArray(env, n);
for (unsigned int v=0; v<n; v++) {
std::stringstream ss;
ss << u;
ss << v;
std::string str = "z" + ss.str();
z[u][v] = IloIntVar(env, 0, 1, str.c_str());
}
}
/* Constant M*/
int M=n*max_d;
UB = M;
/* Objective*/
model.add(IloMinimize(env, lambda));
//model.add(IloMinimize(env, IloSum(c)));
/*Constrains*/
model.add(lambda - UB <= 0);
/* d=function of the distance */
IloArray<IloNumArray> Par_d(env,n);
for (unsigned int u=0; u<n; u++) {
Par_d[u]=IloNumArray(env,n);
for (unsigned int v=0; v<n; v++) {
Par_d[u][v]=d[u][v];
}
}
for (unsigned u=0; u<n; u++) {
for (unsigned v=0; v<u; v++) {
model.add(c[v]-c[u]+M* z[u][v] >= Par_d[u][v] );
model.add(c[u]-c[v]+M*(1-z[u][v]) >= Par_d[u][v]);
numvar++; // + z[u][v]
}
}
/*
for (unsigned i=0; i<sqrt(n)-1; i=i+1) {
for (unsigned j=0; j<sqrt(n)-1; j=j+1) {
//square lattice
model.add (c[i*sqrt(n)+j] +c[i*sqrt(n)+j+1] +
c[(i+1)*sqrt(n)+j] +c[(i+1)*sqrt(n)+j+1]>= 16-4*sqrt(2)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
//
// triangular lattice
// model.add (c[i*5+j]+c[i*5+j+1] +
// c[(i+1)+j] >= 4); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
// model.add (c[i*sqrt(n)+j]+c[i*sqrt(n)+j+1] +
// c[(i+1)*sqrt(n)+j]+c[(i+1)*sqrt(n)+j+1] >= 22 - 4*sqrt(2)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 4-x
}
}
*/
/*
for (unsigned i=0; i<sqrt(n)-2; i+=3) {
for (unsigned j=0; j<sqrt(n)-2; j+=3) {
// model.add (c[i*sqrt(n)+j] + c[i*sqrt(n)+j+1] + c[i*sqrt(n)+j+2] +
// c[(i+1)*sqrt(n)+j]+ c[(i+1)*sqrt(n)+j+1]+ c[(i+1)*sqrt(n)+j+2] +
// c[(i+2)*sqrt(n)+j]+ c[(i+2)*sqrt(n)+j+1]+ c[(i+2)*sqrt(n)+j+2]
// >= 60-17*sqrt(2)-3*sqrt(5)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 3-x
// model.add (c[i*sqrt(n)+j] + c[i*sqrt(n)+j+1] + c[i*sqrt(n)+j+2] +
// c[(i+1)*sqrt(n)+j]+ c[(i+1)*sqrt(n)+j+1]+ c[(i+1)*sqrt(n)+j+2] +
// c[(i+2)*sqrt(n)+j]+ c[(i+2)*sqrt(n)+j+1]+ c[(i+2)*sqrt(n)+j+2]
// >= 82-8*sqrt(2)-2*sqrt(5)); // Bedingung fuer Quadratischen Gridgraph und d(x) = 4-x
}
}
*/
for (unsigned int v=0; v<n; v++) {
IloExpr expr;
model.add (c[v] <= lambda);
model.add (c[v] >= 0);
expr.end();
}
std::cout << "Number of variables " << numvar << "\n";
/* solve the Model*/
cplex.extract(model);
cplex.exportModel("L-Labeling.lp");
/*
start = clock();
int solveError = cplex.solve();
end = clock ();
*/
IloTimer timer(env);
timer.start();
int solveError = cplex.solve();
timer.stop();
if ( !solveError ) {
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
env.error() << "Failed to optimize LP \n";
exit(1);
}
//Info.time = (double)(end-start)/(double)CLOCKS_PER_SEC;
Info.time = timer.getTime();
std::cout << "STATUS : "<< cplex.getStatus() << "\n";
/* get the solution*/
env.out() << "Solution status = " << cplex.getStatus() << "\n";
numconst = cplex.getNrows();
env.out() << " Number of constraints = " << numconst << "\n";
lambda_G_d=cplex.getObjValue();
env.out() << "Solution value = " << lambda_G_d << "\n";
for (unsigned int u=0; u<n; u++) {
C.push_back(cplex.getValue(c[u]));
std::cout << "c(" << u << ")=" << C[u]<< " ";
}
std::cout <<"\n";
/*
for (unsigned int u=0; u<n; u++) {
for (unsigned int v=0; v<u; v++) {
std::cout<< "z[" << u <<"][" << v << "]="<< cplex.getValue( z[u][v]) << " ";
Z.push_back(cplex.getValue( z[u][v]));
}
std::cout << "\n";
}
std::cout <<"\n";
*/
} // end try
catch (IloException& e) {
std::cerr << "Concert exception caught: " << e << std::endl;
}
catch (...) {
std::cerr << "Unknown exception caught" << std::endl;
}
env.end();
return 0;
}
/** Create the dual of a linear program.
* The function can only dualize programs of the form
* <code>Ax <= b, x >= 0</code>. The data in <code>primalVars</code> and
* <code>dualRows</code> as well as in <code>primalRows</code> and
* <code>dualVars</code> is in 1-to-1-correspondence.
* @param primalObj Objective function of primal problem.
* @param primalVars Variables in primal problem.
* @param primalRows Rows in primal problem.
* @param dualObj Objective function of dual will be stored here.
* @param dualVars All dual variables will be stored here.
* @param dualRows All dual rows will be stored here.
*/
void BendersOpt::makeDual(IloObjective const &primalObj,
IloNumVarArray const &primalVars,
IloRangeArray const &primalRows,
IloObjective *dualObj,
IloNumVarArray *dualVars,
IloRangeArray *dualRows)
{
// To keep the code simple we only support problems
// of the form Ax <= b, b >= 0 here. We leave it as a reader's
// exercise to extend the function to something that can handle
// any kind of linear model.
for (IloInt j = 0; j < primalVars.getSize(); ++j)
if ( primalVars[j].getLB() != 0 ||
primalVars[j].getUB() < IloInfinity )
{
std::stringstream s;
s << "Cannot dualize variable " << primalVars[j];
throw s.str();
}
for (IloInt i = 0; i < primalRows.getSize(); ++i)
if ( primalRows[i].getLB() > -IloInfinity ||
primalRows[i].getUB() >= IloInfinity )
{
std::stringstream s;
s << "Cannot dualize constraint " << primalRows[i];
std::cerr << s.str() << std::endl;
throw s.str();
}
// The dual of
// min/max c^T x
// Ax <= b
// x >= 0
// is
// max/min y^T b
// y^T A <= c
// y <= 0
// We scale y by -1 to get >= 0
IloEnv env = primalVars.getEnv();
IloObjective obj(env, 0.0,
primalObj.getSense() == IloObjective::Minimize ?
IloObjective::Maximize : IloObjective::Minimize);
IloRangeArray rows(env);
IloNumVarArray y(env);
std::map<IloNumVar,IloInt,ExtractableLess<IloNumVar> > v2i;
for (IloInt j = 0; j < primalVars.getSize(); ++j) {
IloNumVar x = primalVars[j];
v2i.insert(std::map<IloNumVar,IloInt,ExtractableLess<IloNumVar> >::value_type(x, j));
rows.add(IloRange(env, -IloInfinity, 0, x.getName()));
}
for (IloExpr::LinearIterator it = primalObj.getLinearIterator(); it.ok(); ++it)
rows[v2i[it.getVar()]].setUB(it.getCoef());
for (IloInt i = 0; i < primalRows.getSize(); ++i) {
IloRange r = primalRows[i];
IloNumColumn col(env);
col += obj(-r.getUB());
for (IloExpr::LinearIterator it = r.getLinearIterator(); it.ok(); ++it)
col += rows[v2i[it.getVar()]](-it.getCoef());
y.add(IloNumVar(col, 0, IloInfinity, IloNumVar::Float, r.getName()));
}
*dualObj = obj;
*dualVars = y;
*dualRows = rows;
}
