int
main (int argc, char **argv)
{
char const *vmconfig = NULL;
// Check command line length (exactly two arguments are required).
if ( argc != 3 ) {
usage (argv[0]);
return -1;
}
// Pick up VMC from command line.
vmconfig = argv[1];
// Solve the model.
int exitcode = 0;
IloEnv env;
try {
// Create and read the model.
IloModel model(env);
IloCplex cplex(model);
IloObjective obj;
IloNumVarArray var(env);
IloRangeArray rng(env);
IloSOS1Array sos1(env);
IloSOS2Array sos2(env);
IloRangeArray lazy(env);
IloRangeArray cuts(env);
cplex.importModel(model, argv[2], obj, var, rng, sos1, sos2,
lazy, cuts);
cplex.extract(model);
if ( lazy.getSize() > 0 ) cplex.addLazyConstraints (lazy);
if ( cuts.getSize() > 0 ) cplex.addUserCuts (cuts);
// Load the virtual machine configuration.
// This will force solve() to use parallel distributed MIP.
cplex.readVMConfig(vmconfig);
// Install logging info callback.
IloNum lastObjVal = (obj.getSense() == IloObjective::Minimize ) ?
IloInfinity : -IloInfinity;
cplex.use(loggingCallback(env, var, -100000, lastObjVal,
cplex.getCplexTime(), cplex.getDetTime()));
// Turn off CPLEX logging
cplex.setParam(IloCplex::Param::MIP::Display, 0);
// Solve the problem and display some results.
if ( cplex.solve() )
env.out() << "Solution value = " << cplex.getObjValue() << endl;
else
env.out() << "No solution" << endl;
env.out() << "Solution status = " << cplex.getStatus() << endl;
// Cleanup.
cplex.end();
model.end();
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
exitcode = -1;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
exitcode = -1;
}
env.end();
return exitcode;
} // END main
/** 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;
}
/** Get the objective sense for this worker's objective. */
IloObjective::Sense getObjectiveSense() const { return obj.getSense(); }
int
main (int argc, char **argv)
{
IloEnv env;
try {
IloModel model(env);
IloCplex cplex(env);
IloBool useLoggingCallback = IloFalse;
IloBool useTimeLimitCallback = IloFalse;
IloBool useAborter = IloFalse;
if (( argc != 3 ) ||
( strchr ("lat", argv[2][0]) == NULL ) ) {
usage (argv[0]);
throw(-1);
}
switch (argv[2][0]) {
case 'l':
useLoggingCallback = IloTrue;
break;
case 't':
useTimeLimitCallback = IloTrue;
break;
case 'a':
useAborter = IloTrue;
break;
default:
break;
}
IloObjective obj;
IloNumVarArray var(env);
IloRangeArray rng(env);
IloSOS1Array sos1(env);
IloSOS2Array sos2(env);
IloRangeArray lazy(env);
IloRangeArray cuts(env);
IloCplex::Aborter myAborter;
cplex.importModel(model, argv[1], obj, var, rng, sos1, sos2,
lazy, cuts);
cplex.extract(model);
if ( lazy.getSize() > 0 ) cplex.addLazyConstraints (lazy);
if ( cuts.getSize() > 0 ) cplex.addUserCuts (cuts);
if ( useLoggingCallback ) {
// Set an overall node limit in case callback conditions
// are not met.
cplex.setParam(IloCplex::Param::MIP::Limits::Nodes, 5000);
IloNum lastObjVal = (obj.getSense() == IloObjective::Minimize ) ?
IloInfinity : -IloInfinity;
cplex.use(loggingCallback(env, var, -100000, lastObjVal,
cplex.getCplexTime(), cplex.getDetTime()));
// Turn off CPLEX logging
cplex.setParam(IloCplex::Param::MIP::Display, 0);
}
else if ( useTimeLimitCallback ) {
cplex.use(timeLimitCallback(env, cplex, IloFalse, cplex.getCplexTime(),
1.0, 10.0));
}
else if ( useAborter ) {
myAborter = IloCplex::Aborter(env);
cplex.use(myAborter);
// Typically, you would pass the Aborter object to
// another thread or pass it to an interrupt handler,
// and monitor for some event to occur. When it does,
// call the Aborter's abort method.
//
// To illustrate its use without creating a thread or
// an interrupt handler, abort immediately by calling
// abort before the solve.
//
myAborter.abort();
}
cplex.solve();
env.out() << endl;
env.out() << "Solution status = " << cplex.getStatus() << endl;
env.out() << "CPLEX status = " << cplex.getCplexStatus() << endl;
}
catch (IloException& e) {
cerr << "Concert exception caught: " << e << endl;
}
catch (...) {
cerr << "Unknown exception caught" << endl;
}
env.end();
return 0;
} // END main
/** Create a new worker.
* The constructor mainly does the following:
* - create an IloCplex instance that refers to a remote worker,
* - load the model in <code>modelfile</code>,
* - setup parameters depending on this worker's index,
* - start an asynchronous solve.
* If anything fails then an exception will be thrown.
* @param env The environment used for instantiating Ilo* objects.
* @param i The index of the worker to be created. This also
* determines the parameter settings to use in this worker.
* @param s A pointer to the global solve state.
* @param transport The transport name for the IloCplex constructor.
* @param argc The argument count for the IloCplex constructor.
* @param argv The array of transport arguments for the IloCplex
* constructor.
* @param modelfile Name of the model to be loaded into the worker.
* @param output The output mode.
* @param objdiff The minimal difference between so that two
* consecutive objective function values are considered
* different.
*/
Worker(IloEnv env, int i, SolveState *s, char const *transport,
int argc, char const **argv, char const *modelfile,
OUTPUT output, double objdiff)
: idx(i), state(s), model(env), cplex(0), handle(0),
primal(IloInfinity), dual(-IloInfinity),
obj(env), x(env), rng(env), infoHandler(this), outb(idx), outs(&outb)
{
try {
// Create remote object, setup output and load the model.
cplex = IloCplex(model, transport, argc, argv);
switch (output) {
case OUTPUT_SILENT:
// Disable output on the output and warning stream.
cplex.setOut(env.getNullStream());
cplex.setWarning(env.getNullStream());
break;
case OUTPUT_PREFIXED:
// Redirect output to our custom stream.
cplex.setOut(outs);
cplex.setWarning(outs);
break;
case OUTPUT_LOG:
// Nothing to do here. By default output is enabled.
break;
}
cplex.importModel(model, modelfile, obj, x, rng);
if ( obj.getSense() == IloObjective::Minimize ) {
primal = -IloInfinity;
dual = IloInfinity;
}
// We set the thread count for each solver to 1 so that we do not
// run into problems if multiple solves are performed on the same
// machine.
cplex.setParam(IloCplex::Param::Threads, 1);
// Each worker runs with a different random seed. This way we
// get different paths through the tree even if the other
// parameter settings are the same.
cplex.setParam(IloCplex::Param::RandomSeed, idx);
// Apply parameter settings.
for (class ParamValue const *vals = settings[idx % NUMSETTINGS].values;
vals->isValid(); ++vals)
vals->apply(cplex);
// Install callback and set objective change.
int status = cplex.userfunction (USERACTION_ADDCALLBACK,
0, NULL, 0, 0, NULL);
if ( status )
throw status;
IloCplex::Serializer s;
s.add(objdiff);
status = cplex.userfunction (USERACTION_CHANGEOBJDIFF,
s.getRawLength(), s.getRawData(),
0, 0, NULL);
if ( status )
throw status;
// Register the handler that will process info messages sent
// from the worker.
cplex.setRemoteInfoHandler(&infoHandler);
// Everything is setup. Launch the asynchronous solve.
handle = cplex.solve(true);
} catch (...) {
// In case of an exception we need to take some special
// cleanup actions. Note that if we get here then the
// solve cannot have been started and we don't need to
// kill or join the asynchronous solve.
if ( cplex.getImpl() )
cplex.end();
rng.end();
x.end();
obj.end();
model.end();
throw;
}
}
