本文整理汇总了C++中IloRangeArray类的典型用法代码示例。如果您正苦于以下问题：C++ IloRangeArray类的具体用法？C++ IloRangeArray怎么用？C++ IloRangeArray使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了IloRangeArray类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: CreateStorageBindingConstraint
void CreateStorageBindingConstraint(IloModel model, BoolVar3DMatrix L, BoolVarMatrix M,
BoolVarMatrix X, BoolVar3DMatrix R, BoolVarMatrix Y,
IloRangeArray c){
IloEnv env = model.getEnv();
//The nested for-loops generate L[p][i][t]
//that is required to linearize equation 16
for(int p = 0; p < L.getSize(); p++){
for(int i = 0; i < n; i++){
for(int t = 0; t < T_MAX; t++){
L[p][i].add(IloBoolVar(env));
c.add(L[p][i][t] - M[p][i] - X[i][t] >= -1);
c.add(L[p][i][t] - M[p][i] - X[i][t] <= 0);
c.add(L[p][i][t] + M[p][i] - X[i][t] <= 1);
c.add(L[p][i][t] - M[p][i] + X[i][t] <= 1);
}
}
}
//Contructing the array R[p][e][t]
for(int p = 0; p < R.getSize(); p++){
for(int e = 0; e < E; e++){
for(int t = 0; t < T_MAX; t++){
R[p][e].add(IloBoolVar(env));
}
}
}
//Encoding the constraint eqn-15
for(int t = 0; t < T_MAX; t++){
IloExprArray sum(env);
for(int e = 0; e < E; e++){
sum.add(IloExpr(env));
for(int p = 0; p < n_m; p++){
sum[e] += R[p][e][t];
}
c.add(sum[e] - Y[e][t] == 0);
}
}
//Encoding the constraint eqn-21
for(int t = 0; t < T_MAX; t++){
IloExprArray sum1(env);
IloExprArray sum2(env);
for(int p = 0; p < n_m; p++){
sum1.add(IloExpr(env));
sum2.add(IloExpr(env));
for(int e = 0; e < E; e++)
sum1[p] += R[p][e][t];
for(int i = 0; i < n; i++)
sum2[p] += L[p][i][t];
c.add(sum1[p] - n_r*sum2[p] <= 0);
}
}
return;
}示例2: 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));
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示例3: populatebynonzero
static void
populatebynonzero (IloModel model, IloIntVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
IloObjective obj = IloMaximize(env);
int n, a;
scanf("%d", &n);
scanf("%d", &a);
//restrição
c.add(IloRange(env, -IloInfinity, a));
//variaveis
for(int i=0 ; i<n; i++){
x.add(IloIntVar(env, 0, 1));
}
/*x.add(IloIntVar(env, 0.0, 40.0));
x.add(IloIntVar(env));
x.add(IloIntVar(env));*/
/*obj.setLinearCoef(x[0], 1.0);
obj.setLinearCoef(x[1], 2.0);
obj.setLinearCoef(x[2], 3.0);*/
/*restricoes*/
for(int i=0 ; i<n; i++){
scanf("%d", &a);
c[0].setLinearCoef(x[i], a);
}
//objetivo
for(int i=0 ; i<n; i++){
scanf("%d", &a);
obj.setLinearCoef(x[i], a);
}
/*c[0].setLinearCoef(x[1], 1.0);
c[0].setLinearCoef(x[2], 1.0);
c[1].setLinearCoef(x[0], 1.0);
c[1].setLinearCoef(x[1], -3.0);
c[1].setLinearCoef(x[2], 1.0);*/
c[0].setName("c1");
for(int i=0; i<n; i++){
char tmp[10];
printf("x%d", i+1);
x[i].setName(tmp);
}
model.add(obj);
model.add(c);
} // END populatebynonzero示例4: generateProblem
void generateProblem(const ILPModel& m, IloModel& model, IloNumVarArray& x, IloRangeArray& con) {
IloEnv env = model.getEnv();
IloObjective obj = (m.obj == MINIMIZE ? IloMinimize(env) : IloMaximize(env));
for (unsigned long v = 0; v < m.numberOfVariables(); ++v) {
switch (m.x[v].type) {
case FLT:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Float));
break;
case BIN:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Bool));
break;
default:
x.add(IloNumVar(env, m.x[v].lowerBound, m.x[v].upperBound, IloNumVar::Int));
}
obj.setLinearCoef(x[v], m.c[v]);
x[v].setName(m.varDesc[v].c_str());
}
for (unsigned long c = 0; c < m.numberOfConstraints(); ++c) {
switch (m.ops[c]) {
case LESS_EQUAL:
con.add(IloRange(env, -IloInfinity, m.b[c]));
break;
case EQUAL:
con.add(IloRange(env, m.b[c], m.b[c]));
break;
case GREATER_EQUAL:
con.add(IloRange(env, m.b[c], IloInfinity));
}
for (const pair<uint32_t, double>& p : m.A[c])
con[c].setLinearCoef(x[p.first], p.second);
con[c].setName(m.conDesc[c].c_str());
}
model.add(obj);
model.add(con);
}示例5: CreateMixingBindingConstraint
void CreateMixingBindingConstraint(IloModel model, BoolVarMatrix M, BoolVarMatrix Y,
BoolVarMatrix X, IloNumVarArray s, IloRangeArray c){
IloEnv env = model.getEnv();
//sum[i] holds summation from
//eqn-13 for operation i
IloExprArray sum1(env);
//Creating array M[p][i]
//i is for ith operation
for(int p = 0; p < M.getSize(); p++){
for(int i = 0; i < n; i++)
M[p].add(IloBoolVar(env));
}
//Ensuring operation remains bound to the same module
for(int i = 0; i < n; i++){
sum1.add(IloExpr(env));
for(int p = 0; p < n_m; p++){
sum1[i] += M[p][i];
}
c.add(sum1[i] == 1);
}
//Two operations running simulateneously
//can not be bound to the same module
for(int p = 0; p < n_m; p++){
for(int t = 0; t < T_MAX; t++){
for(int i = 0; i < n; i++){
for(int j = i+1; j < n; j++){
c.add(X[i][t] + X[j][t] + M[p][i] + M[p][j] <= 3);
}
}
}
}
return;
}示例6: CreateSchedulingConstraint
void CreateSchedulingConstraint(IloModel model, BoolVarMatrix X, BoolVarMatrix Y,
IloNumVarArray s, IloRangeArray c){
IloEnv env = model.getEnv();
//sum[i] holds the summation
//from eqn-8 for operation i
IloExprArray sum(env);
//The for-loop encodes all
//the execution constraints
for(int i = 0; i < X.getSize(); i++){
sum.add(IloExpr(env));
for(int t = 0; t < T_MAX; t++){
X[i].add(IloBoolVar(env));
sum[i] += X[i][t];
c.add( t - s[i+1] - T_MAX*(X[i][t]-1) >= 0);
c.add(-t + s[i+1] - T_MAX*(X[i][t]-1) + T >= 1);
}
c.add(sum[i] == T);
}
//Resources Constraints
IloExprArray summation1(env);
IloExprArray summation2(env);
for(int t = 0; t < T_MAX; t++){
summation1.add(IloExpr(env));
summation2.add(IloExpr(env));
for(int i = 0; i < X.getSize(); i++){
summation1[t] += X[i][t];
}
for(int e = 0; e < Y.getSize(); e++){
summation2[t] += Y[e][t];
}
c.add(n_r*summation1[t] + summation2[t] <= n_m*n_r);
}
return;
}示例7: populatebyrow
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示例8: 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, 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示例9: populatebycolumn
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示例10: 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示例11: createmodel
// This function creates the following model:
// Minimize
// obj: x1 + x2 + x3 + x4 + x5 + x6
// Subject To
// c1: x1 + x2 + x5 = 8
// c2: x3 + x5 + x6 = 10
// q1: [ -x1^2 + x2^2 + x3^2 ] <= 0
// q2: [ -x4^2 + x5^2 ] <= 0
// Bounds
// x2 Free
// x3 Free
// x5 Free
// End
// which is a second order cone program in standard form.
// The function returns objective, variables and constraints in the
// values obj, vars and rngs.
// The function also sets up cone so that for a column j we have
// cone[j] >= 0 Column j is in a cone constraint and is the
// cone's head variable.
// cone[j] == NOT_CONE_HEAD Column j is in a cone constraint but is
// not the cone's head variable..
// cone[j] == NOT_IN_CONE Column j is not contained in any cone constraint.
static void
createmodel (IloModel& model, IloObjective &obj, IloNumVarArray &vars,
IloRangeArray &rngs, IloIntArray& cone)
{
// The indices we assign as user objects to the modeling objects.
// We define them as static data so that we don't have to worry about
// dynamic memory allocation/leakage.
static int indices[] = { 0, 1, 2, 3, 4, 5, 6 };
IloEnv env = model.getEnv();
// Create variables.
IloNumVar x1(env, 0, IloInfinity, "x1");
IloNumVar x2(env, -IloInfinity, IloInfinity, "x2");
IloNumVar x3(env, -IloInfinity, IloInfinity, "x3");
IloNumVar x4(env, 0, IloInfinity, "x4");
IloNumVar x5(env, -IloInfinity, IloInfinity, "x5");
IloNumVar x6(env, 0, IloInfinity, "x6");
// Create objective function and immediately store it in return value.
obj = IloMinimize(env, x1 + x2 + x3 + x4 + x5 + x6);
// Create constraints.
IloRange c1(env, 8, x1 + x2 + x5, 8, "c1");
IloRange c2(env, 10, x3 + x5 + x6, 10, "c2");
IloRange q1(env, -IloInfinity, -x1*x1 + x2*x2 + x3*x3, 0, "q1");
cone.add(2); // x1, cone head of constraint at index 2
cone.add(NOT_CONE_HEAD); // x2
cone.add(NOT_CONE_HEAD); // x3
IloRange q2(env, -IloInfinity, -x4*x4 + x5*x5, 0, "q2");
cone.add(3); // x4, cone head of constraint at index 3
cone.add(NOT_CONE_HEAD); // x5
cone.add(NOT_IN_CONE); // x6
// Setup model.
model.add(obj);
model.add(obj);
model.add(c1);
model.add(c2);
model.add(q1);
model.add(q2);
// Setup return values.
vars.add(x1);
vars.add(x2);
vars.add(x3);
vars.add(x4);
vars.add(x5);
vars.add(x6);
rngs.add(c1);
rngs.add(c2);
rngs.add(q1);
rngs.add(q2);
// We set the user object for each modeling object to its index in the
// respective array. This makes the code in checkkkt a little simpler.
for (IloInt i = 0; i < vars.getSize(); ++i)
vars[i].setObject(&indices[i]);
for (IloInt i = 0; i < rngs.getSize(); ++i)
rngs[i].setObject(&indices[i]);
}示例12: checkkkt
// Test KKT conditions on the solution.
// The function returns true if the tested KKT conditions are satisfied
// and false otherwise.
// The function assumes that the model currently extracted to CPLEX is fully
// described by obj, vars and rngs.
static bool
checkkkt (IloCplex& cplex, IloObjective const& obj, IloNumVarArray const& vars,
IloRangeArray const& rngs, IloIntArray const& cone, double tol)
{
IloEnv env = cplex.getEnv();
IloModel model = cplex.getModel();
IloNumArray x(env), dslack(env);
IloNumArray pi(env, rngs.getSize()), slack(env);
// Read primal and dual solution information.
cplex.getValues(x, vars);
cplex.getSlacks(slack, rngs);
// pi for second order cone constraints.
getsocpconstrmultipliers(cplex, vars, rngs, pi, dslack);
// pi for linear constraints.
for (IloInt i = 0; i < rngs.getSize(); ++i) {
IloRange r = rngs[i];
if ( !r.getQuadIterator().ok() )
pi[idx(r)] = cplex.getDual(r);
}
// Print out the data we just fetched.
streamsize oprec = env.out().precision(3);
ios_base::fmtflags oflags = env.out().setf(ios::fixed | ios::showpos);
env.out() << "x = [";
for (IloInt i = 0; i < x.getSize(); ++i)
env.out() << " " << x[i];
env.out() << " ]" << endl;
env.out() << "dslack = [";
for (IloInt i = 0; i < dslack.getSize(); ++i)
env.out() << " " << dslack[i];
env.out() << " ]" << endl;
env.out() << "pi = [";
for (IloInt i = 0; i < rngs.getSize(); ++i)
env.out() << " " << pi[i];
env.out() << " ]" << endl;
env.out() << "slack = [";
for (IloInt i = 0; i < rngs.getSize(); ++i)
env.out() << " " << slack[i];
env.out() << " ]" << endl;
env.out().precision(oprec);
env.out().flags(oflags);
// Test primal feasibility.
// This example illustrates the use of dual vectors returned by CPLEX
// to verify dual feasibility, so we do not test primal feasibility
// here.
// Test dual feasibility.
// We must have
// - for all <= constraints the respective pi value is non-negative,
// - for all >= constraints the respective pi value is non-positive,
// - the dslack value for all non-cone variables must be non-negative.
// Note that we do not support ranged constraints here.
for (IloInt i = 0; i < vars.getSize(); ++i) {
IloNumVar v = vars[i];
if ( cone[i] == NOT_IN_CONE && dslack[i] < -tol ) {
env.error() << "Dual multiplier for " << v << " is not feasible: "
<< dslack[i] << endl;
return false;
}
}
for (IloInt i = 0; i < rngs.getSize(); ++i) {
IloRange r = rngs[i];
if ( fabs (r.getLB() - r.getUB()) <= tol ) {
// Nothing to check for equality constraints.
}
else if ( r.getLB() > -IloInfinity && pi[i] > tol ) {
env.error() << "Dual multiplier " << pi[i] << " for >= constraint"
<< endl << r << endl
<< "not feasible"
<< endl;
return false;
}
else if ( r.getUB() < IloInfinity && pi[i] < -tol ) {
env.error() << "Dual multiplier " << pi[i] << " for <= constraint"
<< endl << r << endl
<< "not feasible"
<< endl;
return false;
}
}
// Test complementary slackness.
// For each constraint either the constraint must have zero slack or
// the dual multiplier for the constraint must be 0. We must also
// consider the special case in which a variable is not explicitly
// contained in a second order cone constraint.
for (IloInt i = 0; i < vars.getSize(); ++i) {
if ( cone[i] == NOT_IN_CONE ) {
if ( fabs(x[i]) > tol && dslack[i] > tol ) {
env.error() << "Invalid complementary slackness for " << vars[i]
<< ":" << endl
//.........这里部分代码省略.........
示例13: obj
/** 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;
}示例14: env
/** Extract sub block number <code>n</code> from <code>problem</code>.
* The constructor creates a representation of block number <code>n</code>
* as described in <code>problem</code>.
* The constructor will also connect the newly created block to a remote
* object solver instance.
* @param problem The problem from which the block is to be extracted.
* @param n Index of the block to be extracted.
* @param argc Argument for IloCplex constructor.
* @param argv Argument for IloCplex constructor.
* @param machines List of machines to which to connect. If the code is
* compiled for the TCP/IP transport then the block will
* be connected to <code>machines[n]</code>.
*/
BendersOpt::Block::Block(Problem const *problem, IloInt n, int argc, char const *const *argv,
std::vector<char const *> const &machines)
: env(), number(n), vars(0), rows(0), cplex(0), cb(0)
{
IloNumVarArray problemVars = problem->getVariables();
IloRangeArray problemRanges = problem->getRows();
// Create a map that maps variables in the original model to their
// respective index in problemVars.
std::map<IloNumVar,IloInt,ExtractableLess<IloNumVar> > origIdxMap;
for (IloInt j = 0; j < problemVars.getSize(); ++j)
origIdxMap.insert(std::map<IloNumVar,IloInt,ExtractableLess<IloNumVar> >::value_type(problemVars[j], j));
// Copy non-fixed variables from original problem into primal problem.
IloExpr primalObj(env);
IloNumVarArray primalVars(env);
IloRangeArray primalRows(env);
IdxMap idxMap; // Index of original variable in block's primal model
RowSet rowSet;
for (IloInt j = 0; j < problemVars.getSize(); ++j) {
IloNumVar x = problemVars[j];
if ( problem->getBlock(x) == number ) {
// Create column in block LP with exactly the same data.
if ( x.getType() != IloNumVar::Float ) {
std::stringstream s;
s << "Cannot create non-continuous block variable " << x;
std::cerr << s.str() << std::endl;
throw s.str();
}
IloNumVar v(env, x.getLB(), x.getUB(), x.getType(), x.getName());
// Normalize objective function to 'minimize'
double coef = problem->getObjCoef(x);
if ( problem->getObjSense() != IloObjective::Minimize )
coef *= -1.0;
primalObj += coef * v;
// Record the index that the copied variable has in the
// block model.
idxMap.insert(IdxMap::value_type(x, primalVars.getSize()));
primalVars.add(v);
// Mark the rows that are intersected by this column
// so that we can collect them later.
RowSet const &intersected = problem->getIntersectedRows(x);
for (RowSet::const_iterator it = intersected.begin();
it != intersected.end(); ++it)
rowSet.insert(*it);
}
else
idxMap.insert(IdxMap::value_type(x, -1));
}
// Now copy all rows that intersect block variables.
for (IloInt i = 0; i < problemRanges.getSize(); ++i) {
IloRange r = problemRanges[i];
if ( rowSet.find(r) == rowSet.end() )
continue;
// Create a copy of the row, normalizing it to '<='
double factor = 1.0;
if ( r.getLB() > -IloInfinity )
factor = -1.0;
IloRange primalR(env,
factor < 0 ? -r.getUB() : r.getLB(),
factor < 0 ? -r.getLB() : r.getUB(), r.getName());
IloExpr lhs(env);
for (IloExpr::LinearIterator it = r.getLinearIterator(); it.ok(); ++it)
{
IloNumVar v = it.getVar();
double const val = factor * it.getCoef();
if ( problem->getBlock(v) != number ) {
// This column is not explicitly in this block. This means
// that it is a column that will be fixed by the master.
// We collect all such columns so that we can adjust the
// dual objective function according to concrete fixings.
// Store information about variables in this block that
// will be fixed by master solves.
fixed.push_back(FixData(primalRows.getSize(), origIdxMap[v], -val));
}
else {
// The column is an ordinary in this block. Just copy it.
lhs += primalVars[idxMap[v]] * val;
}
}
primalR.setExpr(lhs);
primalRows.add(primalR);
lhs.end();
//.........这里部分代码省略.........
示例15: populatebyrow
void LpSolver::populatebyrow (CplexConverter& cplexConverter,
IloModel model, IloNumVarArray x, IloRangeArray c)
{
IloEnv env = model.getEnv();
// CAPITAL LETTERS MEAN I NEED YOUR HELP, here is help
// IloExpr cost(env);
// Create Variables
// cout << "size of var: " << cplexConverter.variables.size() << endl;
for (int i = 0; i < cplexConverter.variables.size(); ++i){
IloNumVar iloVar(env, 0.0, cplexConverter.capacities[i], IloNumVar::Int);
// cout << iloVar << endl;
x.add(iloVar);
}
//Capacity Constraints
for (auto &it : cplexConverter.atomicIdToVarIdDict){
IloExpr t(env);
// cout << "adding constraint ";
for (int j = 0; j < it.second.size(); j++){
// cout << "x[" << it.second[j] << "] + ";
t += x[it.second[j]];
}
// cout << endl;
c.add(t <= cplexConverter.graph->atomicEdges[it.first]->capacity);
// cout << c << endl;
t.end();
}
// other constraints
for (auto nodePair : cplexConverter.graph->nodes){
// For all nodes
Node* n = nodePair.second;
if(n == cplexConverter.src){
// source constraints
// IloExpr inFlow(env);
IloExpr outFlow(env);
for(auto &atoIn : n->atomicEdge_in){
int aeId = atoIn.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
outFlow += x[vId];
// cost += cplexConverter.graph->atomicEdges[cplexConverter.variables[vId].atomicEdgeId]->interest_rate * x[vId];
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
// inFlow += x[vId];
c.add(x[vId] == 0);
// cost -= cplexConverter.graph->atomicEdges[cplexConverter.variables[vId].atomicEdgeId]->interest_rate * x[vId];
}
}
c.add(outFlow == cplexConverter.request);
// inFlow.end();
outFlow.end();
} else if(n == cplexConverter.dest){
// destination constraints
IloExpr inFlow(env);
// IloExpr outFlow(env);
for(auto &atoIn : n->atomicEdge_in){
int aeId = atoIn.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
// outFlow += x[vId];
c.add(x[vId] == 0);
}
}
for (auto &atoOut : n->atomicEdge_out){
int aeId = atoOut.second->atomicEdgeId;
for (int j = 0; j < cplexConverter.atomicIdToVarIdDict[aeId].size(); j++){
// var Id
int vId = cplexConverter.atomicIdToVarIdDict[aeId][j];
inFlow += x[vId];
}
}
c.add(inFlow == cplexConverter.request);
inFlow.end();
// outFlow.end();
} else {
// Monotonicity Constraints
for (int i = 0; i < credNetConstants.totalIrs.size(); ++i){
IloExpr tempin(env);
IloExpr tempout(env);
for (auto &atoIn : n->atomicEdge_in){
//.........这里部分代码省略.........