int
main (void)
{
int status;
CPXENVptr env;
CPXLPptr lp;
CPXDIM i;
double x[NUMCOLS];
double cpi[NUMCOLS];
double rpi[NUMROWS];
double qpi[NUMQS];
double slack[NUMROWS], qslack[NUMQS];
double kktsum[NUMCOLS];
/* ********************************************************************** *
* *
* S E T U P P R O B L E M *
* *
* ********************************************************************** */
/* Create CPLEX environment and enable screen output.
*/
env = CPXXopenCPLEX (&status);
if ( status != 0 )
goto TERMINATE;
status = CPXXsetintparam (env, CPXPARAM_ScreenOutput, CPX_ON);
if ( status != 0 )
goto TERMINATE;
/* Create the problem object and populate it.
*/
lp = CPXXcreateprob (env, &status, "qcpdual");
if ( status != 0 )
goto TERMINATE;
status = CPXXnewcols (env, lp, NUMCOLS, obj, lb, ub, NULL, cname);
if ( status != 0 )
goto TERMINATE;
status = CPXXaddrows (env, lp, 0, NUMROWS, NUMNZS, rhs, sense,
rmatbeg, rmatind, rmatval, NULL, rname);
if ( status != 0 )
goto TERMINATE;
for (i = 0; i < NUMQS; ++i) {
CPXNNZ const linend = (i == NUMQS - 1) ? NUMLINNZ : linbeg[i + 1];
CPXNNZ const quadend = (i == NUMQS - 1) ? NUMQUADNZ : quadbeg[i + 1];
status = CPXXaddqconstr (env, lp, linend - linbeg[i],
quadend - quadbeg[i], qrhs[i], qsense[i],
&linind[linbeg[i]], &linval[linbeg[i]],
&quadrow[quadbeg[i]], &quadcol[quadbeg[i]],
&quadval[quadbeg[i]], qname[i]);
if ( status != 0 )
goto TERMINATE;
}
/* ********************************************************************** *
* *
* O P T I M I Z E P R O B L E M *
* *
* ********************************************************************** */
status = CPXXsetdblparam (env, CPXPARAM_Barrier_QCPConvergeTol, 1e-10);
if ( status != 0 )
goto TERMINATE;
/* Solve the problem.
*/
status = CPXXbaropt (env, lp);
if ( status != 0 )
goto TERMINATE;
if ( CPXXgetstat (env, lp) != CPX_STAT_OPTIMAL ) {
fprintf (stderr, "No optimal solution found!\n");
goto TERMINATE;
}
/* ********************************************************************** *
* *
* Q U E R Y S O L U T I O N *
* *
* ********************************************************************** */
/* Optimal solution and slacks for linear and quadratic constraints. */
status = CPXXgetx (env, lp, x, 0, NUMCOLS - 1);
if ( status != 0 )
goto TERMINATE;
status = CPXXgetslack (env, lp, slack, 0, NUMROWS - 1);
if ( status != 0 )
goto TERMINATE;
status = CPXXgetqconstrslack (env, lp, qslack, 0, NUMQS - 1);
if ( status != 0 )
goto TERMINATE;
/* Dual multipliers for linear constraints and bound constraints. */
status = CPXXgetdj (env, lp, cpi, 0, NUMCOLS - 1);
if ( status != 0 )
goto TERMINATE;
status = CPXXgetpi (env, lp, rpi, 0, NUMROWS - 1);
if ( status != 0 )
goto TERMINATE;
status = getqconstrmultipliers (env, lp, x, qpi, ZEROTOL);
if ( status != 0 )
goto TERMINATE;
/* ********************************************************************** *
* *
* C H E C K K K T C O N D I T I O N S *
* *
* Here we verify that the optimal solution computed by CPLEX (and *
* the qpi[] values computed above) satisfy the KKT conditions. *
* *
* ********************************************************************** */
/* Primal feasibility: This example is about duals so we skip this test. */
/* Dual feasibility: We must verify
* - for <= constraints (linear or quadratic) the dual
* multiplier is non-positive.
* - for >= constraints (linear or quadratic) the dual
* multiplier is non-negative.
*/
for (i = 0; i < NUMROWS; ++i) {
switch (sense[i]) {
case 'E': /* nothing */ break;
case 'R': /* nothing */ break;
case 'L':
if ( rpi[i] > ZEROTOL ) {
fprintf (stderr,
"Dual feasibility test failed for <= row %d: %f\n",
i, rpi[i]);
status = -1;
goto TERMINATE;
}
break;
case 'G':
if ( rpi[i] < -ZEROTOL ) {
fprintf (stderr,
"Dual feasibility test failed for >= row %d: %f\n",
i, rpi[i]);
status = -1;
goto TERMINATE;
}
break;
}
}
for (i = 0; i < NUMQS; ++i) {
switch (qsense[i]) {
case 'E': /* nothing */ break;
case 'L':
if ( qpi[i] > ZEROTOL ) {
fprintf (stderr,
"Dual feasibility test failed for <= quad %d: %f\n",
i, qpi[i]);
status = -1;
goto TERMINATE;
}
break;
case 'G':
if ( qpi[i] < -ZEROTOL ) {
fprintf (stderr,
"Dual feasibility test failed for >= quad %d: %f\n",
i, qpi[i]);
status = -1;
goto TERMINATE;
}
break;
}
}
/* Complementary slackness.
* For any constraint the product of primal slack and dual multiplier
* must be 0.
*/
for (i = 0; i < NUMROWS; ++i) {
if ( sense[i] != 'E' && fabs (slack[i] * rpi[i]) > ZEROTOL ) {
fprintf (stderr,
"Complementary slackness test failed for row %d: %f\n",
i, fabs (slack[i] * rpi[i]));
status = -1;
goto TERMINATE;
}
}
for (i = 0; i < NUMQS; ++i) {
if ( qsense[i] != 'E' && fabs (qslack[i] * qpi[i]) > ZEROTOL ) {
fprintf (stderr,
"Complementary slackness test failed for quad %d: %f\n",
i, fabs (qslack[i] * qpi[i]));
status = -1;
goto TERMINATE;
}
}
for (i = 0; i < NUMCOLS; ++i) {
if ( ub[i] < CPX_INFBOUND ) {
double const slk = ub[i] - x[i];
double const dual = cpi[i] < -ZEROTOL ? cpi[i] : 0.0;
if ( fabs (slk * dual) > ZEROTOL ) {
fprintf (stderr,
"Complementary slackness test failed for ub %d: %f\n",
i, fabs (slk * dual));
status = -1;
goto TERMINATE;
}
}
if ( lb[i] > -CPX_INFBOUND ) {
double const slk = x[i] - lb[i];
double const dual = cpi[i] > ZEROTOL ? cpi[i] : 0.0;
if ( fabs (slk * dual) > ZEROTOL ) {
printf ("lb=%f, x=%f, cpi=%f\n", lb[i], x[i], cpi[i]);
fprintf (stderr,
"Complementary slackness test failed for lb %d: %f\n",
i, fabs (slk * dual));
status = -1;
goto TERMINATE;
}
}
}
/* Stationarity.
* The difference between objective function and gradient at optimal
* solution multiplied by dual multipliers must be 0, i.e., for the
* optimal solution x
* 0 == c
* - sum(r in rows) r'(x)*rpi[r]
* - sum(q in quads) q'(x)*qpi[q]
* - sum(c in cols) b'(x)*cpi[c]
* where r' and q' are the derivatives of a row or quadratic constraint,
* x is the optimal solution and rpi[r] and qpi[q] are the dual
* multipliers for row r and quadratic constraint q.
* b' is the derivative of a bound constraint and cpi[c] the dual bound
* multiplier for column c.
*/
/* Objective function. */
for (i = 0; i < NUMCOLS; ++i)
kktsum[i] = obj[i];
/* Linear constraints.
* The derivative of a linear constraint ax - b (<)= 0 is just a.
*/
for (i = 0; i < NUMROWS; ++i) {
CPXNNZ const end = (i == NUMROWS - 1) ? NUMNZS : rmatbeg[i + 1];
CPXNNZ k;
for (k = rmatbeg[i]; k < end; ++k)
kktsum[rmatind[k]] -= rpi[i] * rmatval[k];
}
/* Quadratic constraints.
* The derivative of a constraint xQx + ax - b <= 0 is
* Qx + Q'x + a.
*/
for (i = 0; i < NUMQS; ++i) {
CPXDIM j;
CPXNNZ k;
for (j = linbeg[i]; j < linbeg[i] + linnzcnt[i]; ++j)
kktsum[linind[j]] -= qpi[i] * linval[j];
for (k = quadbeg[i]; k < quadbeg[i] + quadnzcnt[i]; ++k) {
kktsum[quadrow[k]] -= qpi[i] * x[quadcol[k]] * quadval[k];
kktsum[quadcol[k]] -= qpi[i] * x[quadrow[k]] * quadval[k];
}
}
/* Bounds.
* The derivative for lower bounds is -1 and that for upper bounds
* is 1.
* CPLEX already returns dj with the appropriate sign so there is
* no need to distinguish between different bound types here.
*/
for (i = 0; i < NUMCOLS; ++i) {
kktsum[i] -= cpi[i];
}
for (i = 0; i < NUMCOLS; ++i) {
if ( fabs (kktsum[i]) > ZEROTOL ) {
fprintf (stderr, "Stationarity test failed at index %d: %f\n",
i, kktsum[i]);
status = -1;
goto TERMINATE;
}
}
/* KKT conditions satisfied. Dump out the optimal solutions and
* the dual values.
*/
printf ("Optimal solution satisfies KKT conditions.\n");
printf (" x[] =");
for (i = 0; i < NUMCOLS; ++i)
printf (" %7.3f", x[i]);
printf ("\n");
printf ("cpi[] =");
for (i = 0; i < NUMCOLS; ++i)
printf (" %7.3f", cpi[i]);
printf ("\n");
printf ("rpi[] =");
for (i = 0; i < NUMROWS; ++i)
printf (" %7.3f", rpi[i]);
printf ("\n");
printf ("qpi[] =");
for (i = 0; i < NUMQS; ++i)
printf (" %7.3f", qpi[i]);
printf ("\n");
TERMINATE:
/* ********************************************************************** *
* *
* C L E A N U P *
* *
* ********************************************************************** */
status = CPXXfreeprob (env, &lp);
if ( status != 0 ) {
fprintf (stderr, "WARNING: Failed to free problem: %d\n", status);
}
status = CPXXcloseCPLEX (&env);
if ( status != 0 ) {
fprintf (stderr, "WARNING: Failed to close CPLEX: %d\n", status);
}
return status;
}
int
main (int argc, char **argv)
{
int result = 0;
IloEnv env;
try {
static int indices[] = { 0, 1, 2, 3, 4, 5, 6 };
IloModel model(env);
/* ***************************************************************** *
* *
* S E T U P P R O B L E M *
* *
* The model we setup here is *
* Minimize *
* obj: 3x1 - x2 + 3x3 + 2x4 + x5 + 2x6 + 4x7 *
* Subject To *
* c1: x1 + x2 = 4 *
* c2: x1 + x3 >= 3 *
* c3: x6 + x7 <= 5 *
* c4: -x1 + x7 >= -2 *
* q1: [ -x1^2 + x2^2 ] <= 0 *
* q2: [ 4.25x3^2 -2x3*x4 + 4.25x4^2 - 2x4*x5 + 4x5^2 ] + 2 x1 <= 9.0
* q3: [ x6^2 - x7^2 ] >= 4 *
* Bounds *
* 0 <= x1 <= 3 *
* x2 Free *
* 0 <= x3 <= 0.5 *
* x4 Free *
* x5 Free *
* x7 Free *
* End *
* *
* ***************************************************************** */
IloNumVarArray x(env);
x.add(IloNumVar(env, 0, 3, "x1"));
x.add(IloNumVar(env, -IloInfinity, IloInfinity, "x2"));
x.add(IloNumVar(env, 0, 0.5, "x3"));
x.add(IloNumVar(env, -IloInfinity, IloInfinity, "x4"));
x.add(IloNumVar(env, -IloInfinity, IloInfinity, "x5"));
x.add(IloNumVar(env, 0, IloInfinity, "x6"));
x.add(IloNumVar(env, -IloInfinity, IloInfinity, "x7"));
for (IloInt i = 0; i < x.getSize(); ++i)
x[i].setObject(&indices[i]);
IloObjective obj = IloMinimize(env,
3*x[0] - x[1] + 3*x[2] + 2*x[3] +
x[4] + 2*x[5] + 4*x[6], "obj");
model.add(obj);
IloRangeArray linear(env);
linear.add(IloRange(env, 4.0, x[0] + x[1], 4.0, "c1"));
linear.add(IloRange(env, 3.0, x[0] + x[2], IloInfinity, "c2"));
linear.add(IloRange(env, -IloInfinity, x[5] + x[6], 5.0, "c3"));
linear.add(IloRange(env, -2.0, -x[0] + x[6], IloInfinity, "c4"));
for (IloInt i = 0; i < linear.getSize(); ++i)
linear[i].setObject(&indices[i]);
model.add(linear);
IloRangeArray quad(env);
quad.add(IloRange(env, -IloInfinity, -x[0]*x[0] + x[1] * x[1], 0, "q1"));
quad.add(IloRange(env, -IloInfinity,
4.25*x[2]*x[2] - 2*x[2]*x[3] + 4.25*x[3]*x[3] +
-2*x[3]*x[4] + 4*x[4]*x[4] + 2*x[0],
9.0, "q2"));
quad.add(IloRange(env, 4.0, x[5]*x[5] - x[6]*x[6], IloInfinity, "q3"));
for (IloInt i = 0; i < quad.getSize(); ++i)
quad[i].setObject(&indices[i]);
model.add(quad);
/* ***************************************************************** *
* *
* O P T I M I Z E P R O B L E M *
* *
* ***************************************************************** */
IloCplex cplex(model);
cplex.setParam(IloCplex::Param::Barrier::QCPConvergeTol, 1e-10);
cplex.solve();
/* ***************************************************************** *
* *
* Q U E R Y S O L U T I O N *
* *
* ***************************************************************** */
IloNumArray xval(env);
IloNumArray slack(env);
IloNumArray qslack(env);
IloNumArray cpi(env);
IloNumArray rpi(env);
IloNumArray qpi;
cplex.getValues(x, xval);
cplex.getSlacks(slack, linear);
cplex.getSlacks(qslack, quad);
cplex.getReducedCosts(cpi, x);
cplex.getDuals(rpi, linear);
qpi = getqconstrmultipliers(cplex, xval, quad);
/* ***************************************************************** *
* *
* C H E C K K K T C O N D I T I O N S *
* *
* Here we verify that the optimal solution computed by CPLEX *
* (and the qpi[] values computed above) satisfy the KKT *
* conditions. *
* *
* ***************************************************************** */
// Primal feasibility: This example is about duals so we skip this test.
// Dual feasibility: We must verify
// - for <= constraints (linear or quadratic) the dual
// multiplier is non-positive.
// - for >= constraints (linear or quadratic) the dual
// multiplier is non-negative.
for (IloInt i = 0; i < linear.getSize(); ++i) {
if ( linear[i].getLB() <= -IloInfinity ) {
// <= constraint
if ( rpi[i] > ZEROTOL ) {
cerr << "Dual feasibility test failed for <= row " << i
<< ": " << rpi[i] << endl;
result = -1;
}
}
else if ( linear[i].getUB() >= IloInfinity ) {
// >= constraint
if ( rpi[i] < -ZEROTOL ) {
cerr << "Dual feasibility test failed for >= row " << i
<< ":" << rpi[i] << endl;
result = -1;
}
}
else {
// nothing to do for equality constraints
}
}
for (IloInt q = 0; q < quad.getSize(); ++q) {
if ( quad[q].getLB() <= -IloInfinity ) {
// <= constraint
if ( qpi[q] > ZEROTOL ) {
cerr << "Dual feasibility test failed for <= quad " << q
<< ": " << qpi[q] << endl;
result = -1;
}
}
else if ( quad[q].getUB() >= IloInfinity ) {
// >= constraint
if ( qpi[q] < -ZEROTOL ) {
cerr << "Dual feasibility test failed for >= quad " << q
<< ":" << qpi[q] << endl;
result = -1;
}
}
else {
// nothing to do for equality constraints
}
}
// Complementary slackness.
// For any constraint the product of primal slack and dual multiplier
// must be 0.
for (IloInt i = 0; i < linear.getSize(); ++i) {
if ( fabs (linear[i].getUB() - linear[i].getLB()) > ZEROTOL &&
fabs (slack[i] * rpi[i]) > ZEROTOL )
{
cerr << "Complementary slackness test failed for row " << i
<< ": " << fabs (slack[i] * rpi[i]) << endl;
result = -1;
}
}
for (IloInt q = 0; q < quad.getSize(); ++q) {
if ( fabs (quad[q].getUB() - quad[q].getLB()) > ZEROTOL &&
fabs (qslack[q] * qpi[q]) > ZEROTOL )
{
cerr << "Complementary slackness test failed for quad " << q
<< ":" << fabs (qslack[q] * qpi[q]) << endl;
result = -1;
}
}
for (IloInt j = 0; j < x.getSize(); ++j) {
if ( x[j].getUB() < IloInfinity ) {
double const slk = x[j].getUB() - xval[j];
double const dual = cpi[j] < -ZEROTOL ? cpi[j] : 0.0;
if ( fabs (slk * dual) > ZEROTOL ) {
cerr << "Complementary slackness test failed for ub " << j
<< ": " << fabs (slk * dual) << endl;
result = -1;
}
}
if ( x[j].getLB() > -IloInfinity ) {
double const slk = xval[j] - x[j].getLB();
double const dual = cpi[j] > ZEROTOL ? cpi[j] : 0.0;
if ( fabs (slk * dual) > ZEROTOL ) {
cerr << "Complementary slackness test failed for lb " << j
<< ": " << fabs (slk * dual) << endl;
result = -1;
}
}
}
// Stationarity.
// The difference between objective function and gradient at optimal
// solution multiplied by dual multipliers must be 0, i.e., for the
// optimal solution x
// 0 == c
// - sum(r in rows) r'(x)*rpi[r]
// - sum(q in quads) q'(x)*qpi[q]
// - sum(c in cols) b'(x)*cpi[c]
// where r' and q' are the derivatives of a row or quadratic constraint,
// x is the optimal solution and rpi[r] and qpi[q] are the dual
// multipliers for row r and quadratic constraint q.
// b' is the derivative of a bound constraint and cpi[c] the dual bound
// multiplier for column c.
IloNumArray kktsum(env, x.getSize());
// Objective function.
for (IloExpr::LinearIterator it = obj.getLinearIterator(); it.ok(); ++it)
kktsum[idx(it.getVar())] = it.getCoef();
// Linear constraints.
// The derivative of a linear constraint ax - b (<)= 0 is just a.
for (IloInt i = 0; i < linear.getSize(); ++i) {
for (IloExpr::LinearIterator it = linear[i].getLinearIterator();
it.ok(); ++it)
kktsum[idx(it.getVar())] -= rpi[i] * it.getCoef();
}
// Quadratic constraints.
// The derivative of a constraint xQx + ax - b <= 0 is
// Qx + Q'x + a.
for (IloInt q = 0; q < quad.getSize(); ++q) {
for (IloExpr::LinearIterator it = quad[q].getLinearIterator();
it.ok(); ++it)
kktsum[idx(it.getVar())] -= qpi[q] * it.getCoef();
for (IloExpr::QuadIterator it = quad[q].getQuadIterator();
it.ok(); ++it)
{
kktsum[idx(it.getVar1())] -= qpi[q] * xval[idx(it.getVar2())] * it.getCoef();
kktsum[idx(it.getVar2())] -= qpi[q] * xval[idx(it.getVar1())] * it.getCoef();
}
}
// Bounds.
// The derivative for lower bounds is -1 and that for upper bounds
// is 1.
// CPLEX already returns dj with the appropriate sign so there is
// no need to distinguish between different bound types here.
for (IloInt j = 0; j < x.getSize(); ++j)
kktsum[j] -= cpi[j];
for (IloInt j = 0; j < kktsum.getSize(); ++j) {
if ( fabs (kktsum[j]) > ZEROTOL ) {
cerr << "Stationarity test failed at index " << j
<< ": " << kktsum[j] << endl;
result = -1;
}
}
if ( result == 0) {
// KKT conditions satisfied. Dump out the optimal solutions and
// the dual values.
streamsize oprec = cout.precision(3);
ios_base::fmtflags oflags = cout.setf(ios::fixed | ios::showpos);
cout << "Optimal solution satisfies KKT conditions." << endl;
cout << " x[] = " << xval << endl;
cout << " cpi[] = " << cpi << endl;
cout << " rpi[] = " << rpi << endl;
cout << " qpi[] = " << qpi << endl;
cout.precision(oprec);
cout.flags(oflags);
}
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
result = -1;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
result = -1;
}
env.end();
return result;
} // END main
