/* prüft, die Länge der Tour, dabei ist
* sol ein Array, dass die Werte der Kanten enthält (angeordnet als LU Dreieck),
* visited ist ein Hilfsarray, dass sich merkt, welche Punkte schon besucht wurden
* tol ist die numerische Toleranz (vgl. andere Bsp. für Lazy Constraints)
* */
int getTourLen(IloNumArray sol, IloNumArray visited, IloNum tol, tour_t *tour, bool mip)
{
int j;
const int n = sol.getSize();
const int N = sqrt(n);
int last = -1;
int length = 0;
int current = 0;
visited.clear();
visited.add(n, 0.0); // mit 0 initialisieren
// Problemgröße von 0? Das sollte nicht passieren
if(n == 0)
{
printf(ERROR "argghh! zero! zero length LP representation!\n");
return (N+1);
}
// bis ich wieder da ankomme, wo ich gewesen bin
while(visited[current] == 0)
{
length++;
visited[current] = length; // notiere Position auf der Stadt
// suche die nächste Stadt in der Waagerechten des UL Dreiecks
for(j=0; j<current; j++)
if(j != last && sol[current*N + j] >= 1.0-tol)
break;
// wenn ich nicht abgebrochen habe -> nicht gefunden
// suche die nächste Stadt in der Senkrechten des UL Dreiecks
if(j == current)
for(j = current+1; j < N; j++)
if(j != last && sol[j*N + current] >= 1.0-tol)
break;
// es gibt keinen Nachbarn ?! Das sollte nicht passieren.
if(j == N)
{
if(mip)
printf(ERROR "argghh! separated point! no neighbors! everybody panic!\n");
return (N+1);
}
// gehe zur nächsten Stadt
last = current;
current = j;
if(tour != NULL)
tour->push_back(current);
}
// Länge der getroffenen Subtour
return length;
}
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
ILOCPLEXGOAL1(MyBranchGoal, IloNumVarArray, vars) {
IloNumArray x;
IloNumArray obj;
IntegerFeasibilityArray feas;
x = IloNumArray(getEnv());
obj = IloNumArray(getEnv());
feas = IntegerFeasibilityArray(getEnv());
getValues(x, vars);
getObjCoefs(obj, vars);
getFeasibilities(feas, vars);
IloInt bestj = -1;
IloNum maxinf = 0.0;
IloNum maxobj = 0.0;
IloInt cols = vars.getSize();
for (IloInt j = 0; j < cols; j++) {
if ( feas[j] == Infeasible ) {
IloNum xj_inf = x[j] - IloFloor (x[j]);
if ( xj_inf > 0.5 )
xj_inf = 1.0 - xj_inf;
if ( xj_inf >= maxinf &&
(xj_inf > maxinf || IloAbs (obj[j]) >= maxobj) ) {
bestj = j;
maxinf = xj_inf;
maxobj = IloAbs (obj[j]);
}
}
}
IloCplex::Goal res;
if ( bestj >= 0 ) {
res = AndGoal(OrGoal(vars[bestj] >= IloFloor(x[bestj])+1,
vars[bestj] <= IloFloor(x[bestj])),
this);
}
x.end();
obj.end();
feas.end();
return res;
}
ILOHEURISTICCALLBACK1(Rounddown, IloNumVarArray, vars) {
IntegerFeasibilityArray feas;
IloNumArray obj;
IloNumArray x;
try {
feas = IntegerFeasibilityArray(getEnv());
obj = IloNumArray(getEnv());
x = IloNumArray(getEnv());
getFeasibilities(feas, vars);
getObjCoefs (obj , vars);
getValues (x , vars);
IloNum objval = getObjValue();
IloInt cols = vars.getSize();
for (IloInt j = 0; j < cols; j++) {
// Set the fractional variable to zero and update the objective value
if ( feas[j] == Infeasible ) {
objval -= x[j] * obj[j];
x[j] = 0.0;
}
}
setSolution(vars, x, objval);
}
catch (...) {
feas.end();
obj.end();
x.end();
throw;
}
feas.end();
obj.end();
x.end();
}
ILOBRANCHCALLBACK1(MyBranch, IloNumVarArray, vars) {
if ( getBranchType() != BranchOnVariable )
return;
// Branch on var with largest objective coefficient
// among those with largest infeasibility
IloNumArray x;
IloNumArray obj;
IntegerFeasibilityArray feas;
try {
x = IloNumArray(getEnv());
obj = IloNumArray(getEnv());
feas = IntegerFeasibilityArray(getEnv());
getValues(x, vars);
getObjCoefs(obj, vars);
getFeasibilities(feas, vars);
IloInt bestj = -1;
IloNum maxinf = 0.0;
IloNum maxobj = 0.0;
IloInt cols = vars.getSize();
for (IloInt j = 0; j < cols; j++) {
if ( feas[j] == Infeasible ) {
IloNum xj_inf = x[j] - IloFloor (x[j]);
if ( xj_inf > 0.5 )
xj_inf = 1.0 - xj_inf;
if ( xj_inf >= maxinf &&
(xj_inf > maxinf || IloAbs (obj[j]) >= maxobj) ) {
bestj = j;
maxinf = xj_inf;
maxobj = IloAbs (obj[j]);
}
}
}
if ( bestj >= 0 ) {
makeBranch(vars[bestj], x[bestj], IloCplex::BranchUp, getObjValue());
makeBranch(vars[bestj], x[bestj], IloCplex::BranchDown, getObjValue());
}
}
catch (...) {
x.end();
obj.end();
feas.end();
throw;
}
x.end();
obj.end();
feas.end();
}
IloNum sumArray(IloNumArray redCosts) {
// delcare loop counter:
IloInt m;
// default/starting value: 0.
IloNum arrSum = 0.0;
// loop over the array members
for (m = 0; m < redCosts.getSize(); m++) {
// add member by member
arrSum += redCosts[m];
}
// return minimum
return arrSum;
} // END of sumArray
IloNum minMemberValue(IloNumArray redCosts) {
// delcare loop counter:
IloInt m;
// default/starting minimum: 0.
IloNum currentMin = 0.0;
// loop over the array members
for (m = 0; m < redCosts.getSize(); m++) {
// see if current members improves minimum
if (redCosts[m] < currentMin) {
currentMin = redCosts[m];
}
}
// return minimum
return currentMin;
} // END of minMemberValue
void
makeCuts(const IloNumVarArray vars, IloExprArray lhs, IloNumArray rhs) {
IloNumVar x11, x12, x13, x14, x15;
IloNumVar x21, x22, x23, x24, x25;
IloNumVar x31, x32, x33, x34, x35;
IloNumVar x41, x42, x43, x44, x45;
IloNumVar x51, x52, x53, x54, x55;
IloNumVar w11, w12, w13, w14, w15;
IloNumVar w21, w22, w23, w24, w25;
IloNumVar w31, w32, w33, w34, w35;
IloNumVar w41, w42, w43, w44, w45;
IloNumVar w51, w52, w53, w54, w55;
IloInt num = vars.getSize();
for (IloInt i = 0; i < num; i++) {
if ( strcmp(vars[i].getName(), "X11") == 0 ) x11 = vars[i];
else if ( strcmp(vars[i].getName(), "X12") == 0 ) x12 = vars[i];
else if ( strcmp(vars[i].getName(), "X13") == 0 ) x13 = vars[i];
else if ( strcmp(vars[i].getName(), "X14") == 0 ) x14 = vars[i];
else if ( strcmp(vars[i].getName(), "X15") == 0 ) x15 = vars[i];
else if ( strcmp(vars[i].getName(), "X21") == 0 ) x21 = vars[i];
else if ( strcmp(vars[i].getName(), "X22") == 0 ) x22 = vars[i];
else if ( strcmp(vars[i].getName(), "X23") == 0 ) x23 = vars[i];
else if ( strcmp(vars[i].getName(), "X24") == 0 ) x24 = vars[i];
else if ( strcmp(vars[i].getName(), "X25") == 0 ) x25 = vars[i];
else if ( strcmp(vars[i].getName(), "X31") == 0 ) x31 = vars[i];
else if ( strcmp(vars[i].getName(), "X32") == 0 ) x32 = vars[i];
else if ( strcmp(vars[i].getName(), "X33") == 0 ) x33 = vars[i];
else if ( strcmp(vars[i].getName(), "X34") == 0 ) x34 = vars[i];
else if ( strcmp(vars[i].getName(), "X35") == 0 ) x35 = vars[i];
else if ( strcmp(vars[i].getName(), "X41") == 0 ) x41 = vars[i];
else if ( strcmp(vars[i].getName(), "X42") == 0 ) x42 = vars[i];
else if ( strcmp(vars[i].getName(), "X43") == 0 ) x43 = vars[i];
else if ( strcmp(vars[i].getName(), "X44") == 0 ) x44 = vars[i];
else if ( strcmp(vars[i].getName(), "X45") == 0 ) x45 = vars[i];
else if ( strcmp(vars[i].getName(), "X51") == 0 ) x51 = vars[i];
else if ( strcmp(vars[i].getName(), "X52") == 0 ) x52 = vars[i];
else if ( strcmp(vars[i].getName(), "X53") == 0 ) x53 = vars[i];
else if ( strcmp(vars[i].getName(), "X54") == 0 ) x54 = vars[i];
else if ( strcmp(vars[i].getName(), "X55") == 0 ) x55 = vars[i];
else if ( strcmp(vars[i].getName(), "W11") == 0 ) w11 = vars[i];
else if ( strcmp(vars[i].getName(), "W12") == 0 ) w12 = vars[i];
else if ( strcmp(vars[i].getName(), "W13") == 0 ) w13 = vars[i];
else if ( strcmp(vars[i].getName(), "W14") == 0 ) w14 = vars[i];
else if ( strcmp(vars[i].getName(), "W15") == 0 ) w15 = vars[i];
else if ( strcmp(vars[i].getName(), "W21") == 0 ) w21 = vars[i];
else if ( strcmp(vars[i].getName(), "W22") == 0 ) w22 = vars[i];
else if ( strcmp(vars[i].getName(), "W23") == 0 ) w23 = vars[i];
else if ( strcmp(vars[i].getName(), "W24") == 0 ) w24 = vars[i];
else if ( strcmp(vars[i].getName(), "W25") == 0 ) w25 = vars[i];
else if ( strcmp(vars[i].getName(), "W31") == 0 ) w31 = vars[i];
else if ( strcmp(vars[i].getName(), "W32") == 0 ) w32 = vars[i];
else if ( strcmp(vars[i].getName(), "W33") == 0 ) w33 = vars[i];
else if ( strcmp(vars[i].getName(), "W34") == 0 ) w34 = vars[i];
else if ( strcmp(vars[i].getName(), "W35") == 0 ) w35 = vars[i];
else if ( strcmp(vars[i].getName(), "W41") == 0 ) w41 = vars[i];
else if ( strcmp(vars[i].getName(), "W42") == 0 ) w42 = vars[i];
else if ( strcmp(vars[i].getName(), "W43") == 0 ) w43 = vars[i];
else if ( strcmp(vars[i].getName(), "W44") == 0 ) w44 = vars[i];
else if ( strcmp(vars[i].getName(), "W45") == 0 ) w45 = vars[i];
else if ( strcmp(vars[i].getName(), "W51") == 0 ) w51 = vars[i];
else if ( strcmp(vars[i].getName(), "W52") == 0 ) w52 = vars[i];
else if ( strcmp(vars[i].getName(), "W53") == 0 ) w53 = vars[i];
else if ( strcmp(vars[i].getName(), "W54") == 0 ) w54 = vars[i];
else if ( strcmp(vars[i].getName(), "W55") == 0 ) w55 = vars[i];
}
lhs.add(x21 - x22);
rhs.add(0.0);
lhs.add(x22 - x23);
rhs.add(0.0);
lhs.add(x23 - x24);
rhs.add(0.0);
lhs.add(2.08*x11 + 2.98*x21 + 3.47*x31 + 2.24*x41 + 2.08*x51 +
0.25*w11 + 0.25*w21 + 0.25*w31 + 0.25*w41 + 0.25*w51);
rhs.add(20.25);
lhs.add(2.08*x12 + 2.98*x22 + 3.47*x32 + 2.24*x42 + 2.08*x52 +
0.25*w12 + 0.25*w22 + 0.25*w32 + 0.25*w42 + 0.25*w52);
rhs.add(20.25);
lhs.add(2.08*x13 + 2.98*x23 + 3.47*x33 + 2.24*x43 + 2.08*x53 +
0.25*w13 + 0.25*w23 + 0.25*w33 + 0.25*w43 + 0.25*w53);
rhs.add(20.25);
lhs.add(2.08*x14 + 2.98*x24 + 3.47*x34 + 2.24*x44 + 2.08*x54 +
0.25*w14 + 0.25*w24 + 0.25*w34 + 0.25*w44 + 0.25*w54);
rhs.add(20.25);
lhs.add(2.08*x15 + 2.98*x25 + 3.47*x35 + 2.24*x45 + 2.08*x55 +
0.25*w15 + 0.25*w25 + 0.25*w35 + 0.25*w45 + 0.25*w55);
rhs.add(16.25);
}
bool getCut( IloNumArray& vals, IloNumVarArray& vars, CutMode cutmode, int set,
IloCplex::ControlCallbackI::IntegerFeasibilityArray feas,
IloRange& cut, int** graph, int* old_winner ){
int n = vals.getSize()/2;
bool marked[n];
for( int i = 0; i < n; ++i )
marked[i] = false;
int winner;
int count_marked = 0;
list<int> indices;
int acc[n][n];//mudar para lista adjacencia
memset(acc, 0, sizeof(acc));
static int cont=0;
for( int i = 0; i < n; ++i ){
if(feas[i+n*set] == IloCplex::ControlCallbackI::Infeasible)
marked[i] = false;
else{
marked[i]=true;
count_marked++;
}
}
//int cont=0;
int cnt=0;
while( count_marked < n ){
//choose the star node
cnt++;
winner = -1;
for( int i = 0; i < n; ++i ){
if(old_winner[i+n*set])continue;
if( !marked[i] ){
if( winner == -1 ){
winner = i;
}
else if( cutmode == CLQ2B && fabs(vals[i+n*set]-0.5) < fabs(vals[winner+n*set]-0.5) && vals[i+n*set] > 0 && vals[i+n*set] < 1 ){
winner = i;
}
else if( cutmode == CLQ2A && vals[i+n*set] > vals[winner+n*set] && vals[i+n*set] < 1 ){
winner = i;
}
}
}
if(winner==-1)return false; //??
old_winner[winner+n*set]=1;
++count_marked;
marked[winner] = true;
indices.push_back( winner+n*set );
for( int i = 0; i < n; ++i ){
if( !graph[i][winner] ){
if( !marked[i] ){
marked[i] = true;
++count_marked;
}
}
}
}
list<int>::iterator it = indices.begin();
float sum = 0;
while( it != indices.end() ){
sum += vals[*it];
++it;
}
for(list<int>::iterator it1 = indices.begin(); it1!= indices.end(); it1++){
for(list<int>::iterator it2 = indices.begin(); it2!= indices.end(); it2++){
int v1 = *it1;
int v2 = *it2;
if(v1==v2)continue;
v1 -= n*set;
v2 -= n*set;
if(!graph[v1][v2] || !graph[v2][v1]){
puts("ERRO NAO CLICK");
printf("%d %d\n", v1, v2);
exit(-1);
}
}
}
if(sum > 1+0.000001 ){
it = indices.begin();
while( it != indices.end() ){
cut.setLinearCoef( vars[*it], 1 );
//printf("x[%d] ", *it);
++it;
}
//printf(" <= 1\n");
return true;
}
return false;
}
int
main(int argc, char **argv)
{
char const *modelfile = NULL;
int jobs = 0;
char const **machine = 0;
#if defined(USE_MPI)
int numprocs, rank;
// Initialize MPI.
// We must have at least three processors (master and 2 workers) and
// the master must have rank 0.
MPI_Init(&argc, &argv);
MPI_Comm_size (MPI_COMM_WORLD, &numprocs);
if ( numprocs < 3 ) {
fprintf (stderr, "Invalid number of processors (%d)\n", numprocs);
abort ();
}
MPI_Comm_rank (MPI_COMM_WORLD, &rank);
if ( rank != 0 ) {
fprintf (stderr, "Master must have rank 0!\n");
MPI_Finalize ();
abort ();
}
machine = new char const*[numprocs];
for (int i = 0; i < numprocs; ++i)
machine[i] = "mpimachine";
jobs = numprocs - 1;
#elif defined(USE_PROCESS)
// The default binary is CPLEX but that can be overwritten
// by command line arguments.
char const *bin = "cplex";
machine = new char const *[argc];
#elif defined(USE_TCPIP)
machine = new char const *[argc];
#else
# error "No transport type selected"
#endif
// Parse the command line.
Worker::OUTPUT output = Worker::OUTPUT_SILENT;
double absgap = 1e-6;
for (int i = 1; i < argc; ++i) {
if ( strncmp (argv[i], "-model=", 7) == 0 )
modelfile = argv[i] + 7;
#if defined(USE_MPI)
// no MPI specific commands supported
#elif defined(USE_PROCESS)
// For process transport
// - machine=<command> specifies the name of a machine to which
// we connect (via ssh)
// -bin=<binary> specifies the binary to execute (default is "cplex")
else if ( strncmp (argv[i], "-machine=", 9) == 0 )
machine[jobs++] = argv[i] + 9;
else if ( strncmp (argv[i], "-bin=", 5) == 0 )
bin = argv[i] + 5;
#elif defined(USE_TCPIP)
// For TCP/IP process
// -address=<host>:<port> specifies a worker address to which to
// connect
else if ( strncmp (argv[i], "-address=", 9) == 0 )
machine[jobs++] = argv[i];
#endif
// Further arguments
// -absgap=<gap> stop if that absolute gap is reached
// -output-prefixed prefix all worker output by the worker number
// -output-log output worker log messages
else if ( strncmp (argv[i], "-absgap=", 8) == 0 )
absgap = strtod (argv[i] + 8, NULL);
else if ( strcmp (argv[i], "-output-prefixed") == 0 )
output = Worker::OUTPUT_PREFIXED;
else if ( strcmp (argv[i], "-output-log") == 0 )
output = Worker::OUTPUT_LOG;
}
// Validate arguments.
if ( !modelfile )
throw "No model file specified with -model=<modelfile>";
if ( jobs < 1 )
throw "Invalid job count";
// Find the place at which to find the shared object that implements
// the user function. On Windows the path to the current directory is
// likely to contain blanks, so better quote it.
char cwd[MAX_PATH_LEN];
char usrfunc[MAX_PATH_LEN];
getcwd (cwd, sizeof (cwd));
usrfunc[0] = 0;
#ifdef _WIN32
strcat (usrfunc, "-libpath=\"");
strcat (usrfunc, cwd);
strcat (usrfunc, "\"");
#else
strcat (usrfunc, "-libpath=");
strcat (usrfunc, cwd);
#endif
IloEnv env;
SolveState state;
// Initialize the workers.
// The main thing to do here is to set up the connection arguments
// for the IloCplex constructor. Once we have them we just instantiate
// the Worker class. The constructor for this class will also start
// an asynchronous solve immediately.
Worker **workers = new Worker*[jobs];
for (int i = 0; i < jobs; ++i) {
char const *transport = 0;
char const *args[16];
int nextarg = 0;
#if defined(USE_MPI)
char rankbuf[256];
sprintf (rankbuf, "-remoterank=%d", i + 1);
transport = "mpitransport";
args[nextarg++] = rankbuf;
#elif defined(USE_PROCESS)
char logbuf[1024];
transport = "processtransport";
// If the machine is not "localhost" then use ssh to connect to
// this machine. Otherwise just fork a process on the local
// machine.
if ( machine[i] != NULL && strcmp (machine[i], "localhost") != 0 ) {
args[nextarg++] = "/usr/bin/ssh";
args[nextarg++] = machine[i];
}
args[nextarg++] = bin;
args[nextarg++] = "-worker=process";
if ( machine[i] != NULL )
args[nextarg++] = "-stdio";
else
args[nextarg++] = "-namedpipes=.";
args[nextarg++] = usrfunc;
args[nextarg++] = "-userfunction=iloparmipopt_userfunction=REGISTER_USERFUNCTION";
sprintf (logbuf, "-logfile=server%d.log", i);
args[nextarg++] = logbuf;
#elif defined(USE_TCPIP)
transport = "tcpiptransport";
args[nextarg++] = machine[i];
#endif
std::cout << "Initializing worker for " << machine[i] << std::endl;
try {
workers[i] = new Worker(env, i, &state,
transport, nextarg, args,
modelfile, output, 1e-5);
} catch (...) {
while (--i >= 0)
delete workers[i];
delete[] workers;
throw;
}
}
delete[] machine;
try {
// At this point all workers have been started and are
// solving the problem. We just wait until either the first
// worker has finished or if the best known global primal and dual
// bounds are close enough.
IloObjective::Sense const objsen = workers[0]->getObjectiveSense();
int active = jobs;
int frequency = 10000; // Print current bounds every two seconds.
while (active > 0) {
// Check if we should stop all solves.
// We stop them if the absolute mipgap is reached.
if ( state.primal.valid && state.dual.valid &&
((objsen == IloObjective::Minimize &&
state.dual.bound + absgap >= state.primal.bound) ||
(objsen == IloObjective::Maximize &&
state.dual.bound - absgap <= state.primal.bound)) )
{
std::cout << "Stopping criterion reached. Stopping all pending solves."
<< std::endl;
for (int i = 0; i < jobs; ++i)
workers[i]->kill();
break;
}
if ( --frequency == 0 ) {
std::cout << "dual=" << state.dual.bound << ", "
<< "primal=" << state.primal.bound
<< std::endl;
frequency = 10000;
}
// Loop over all solvers and test if they are still running.
for (int i = 0; i < jobs; ++i) {
if ( !workers[i]->isRunning() ) {
// The job is finished. We have a solution, so kill all
// others.
--active;
std::cout << "First job (" << i << ") is finished, killing the rest"
<< std::endl;
for (int j = 0; j < jobs; ++j) {
if ( j != i ) {
workers[j]->kill();
--active;
}
}
break;
}
}
// Sleep a little so that we do not poll the workers like crazy.
millisleep (10);
}
// All workers have finished or have been killed. Join them.
// For each worker we print its status, its dettime and its bounds.
for (int i = 0; i < jobs; ++i) {
double obj = -IloInfinity;
bool const result = workers[i]->join();
if ( !result ) {
// No feasible solution found (yet) on this machine.
// Just set objective function to a very big value
obj = (objsen == IloObjective::Minimize) ? IloInfinity : -IloInfinity;
}
else {
obj = workers[i]->getObjective();
}
std::cout << "Job " << i << ": " << obj << ", stat " << workers[i]->getStatus()
<< std::endl
<< "\t" << workers[i]->getDetTime() << ", "
<< workers[i]->getDual() << ", "
<< workers[i]->getPrimal() << std::endl;
}
// Fetch the x vector from the solver that produced the best
// primal bound.
int const bestidx = state.primal.idx;
if ( bestidx < 0 ) {
std::cout << "No solution (model infeasible)" << std::endl;
}
else {
IloNumArray x = workers[bestidx]->getX();
std::cout << "Optimal solution:" << std::endl;
for (IloInt c = 0; c < x.getSize(); ++c)
std::cout << "x[" << c << "]: " << x[c] << std::endl;
x.end();
}
// Release the workers.
for (int i = jobs - 1; i >= 0; --i)
delete workers[i];
delete[] workers;
env.end();
} catch (...) {
// In case of any error we still need to delete the workers.
// This is to make sure that we properly disconnect from the
// remote workers. The destructor of a worker will automatically
// kill and join the worker if that was not already done.
for (int i = jobs - 1; i >= 0; --i)
delete workers[i];
delete[] workers;
throw;
}
#ifdef USE_MPI
MPI_Finalize ();
#endif
return 0;
}
ILOBRANCHCALLBACK1(SOSbranch, IloSOS1Array, sos) {
IloNumArray x;
IloNumVarArray var;
try {
IloInt i;
x = IloNumArray(getEnv());
var = IloNumVarArray(getEnv());
IloNum bestx = EPS;
IloInt besti = -1;
IloInt bestj = -1;
IloInt num = sos.getSize();
for (i = 0; i < num; i++) {
if ( getFeasibility(sos[i]) == Infeasible ) {
var.clear();
sos[i].getVariables(var);
getValues(x, var);
IloInt n = var.getSize();
for (IloInt j = 0; j < n; j++) {
IloNum inf = IloAbs(x[j] - IloRound(x[j]));
if ( inf > bestx ) {
bestx = inf;
besti = i;
bestj = j;
}
}
}
}
if ( besti >= 0 ) {
IloCplex::BranchDirectionArray dir;
IloNumArray val;
try {
dir = IloCplex::BranchDirectionArray(getEnv());
val = IloNumArray(getEnv());
var.clear();
sos[besti].getVariables(var);
IloInt n = var.getSize();
for (IloInt j = 0; j < n; j++) {
if ( j != bestj ) {
dir.add(IloCplex::BranchDown);
val.add(0.0);
} else {
dir.add(IloCplex::BranchUp);
val.add(1.0);
}
}
makeBranch(var, val, dir, getObjValue());
makeBranch(var[bestj], 0.0, IloCplex::BranchDown, getObjValue());
}
catch (...) {
dir.end();
val.end();
throw;
}
dir.end();
val.end();
}
}
catch (...) {
var.end();
x.end();
throw;
}
var.end();
x.end();
}
