//////////////////////////////////////////////////////////////////////////////
// For a given vector of index columns, check if the GroupBy columns are
// a prefix of the index, in any order.
//////////////////////////////////////////////////////////////////////////////
NABoolean Refresh::areGroupByColsAnIndexPrefix(const NAColumnArray & groupByCols,
const NAColumnArray & columnArray) const
{
if(groupByCols.entries() > columnArray.entries())
return FALSE;
// For nGroupBys GroupBy columns, check that the first nGroupBys columns
// of the index are GroupBy columns.
CollIndex nGroupBys = groupByCols.entries();
for (CollIndex indexCol(0); indexCol < nGroupBys; indexCol++)
{
NABoolean foundIt = FALSE;
const NAString& indexColName = columnArray[indexCol]->getColName();
for (CollIndex groupByCol(0); groupByCol < nGroupBys; groupByCol++)
{
if(groupByCols[groupByCol]->getColName() == indexColName)
{
foundIt = TRUE;
break;
}
}
if (!foundIt)
return FALSE;
}
return TRUE;
} // Refresh::groupByContainedAsPrefix
GtkWidget* FilterEditor::createCriteriaPanel() {
// Create an hbox for the treeview and the action buttons
GtkWidget* hbox = gtk_hbox_new(FALSE, 6);
// Create a new treeview
_ruleView = GTK_TREE_VIEW(gtk_tree_view_new_with_model(GTK_TREE_MODEL(_ruleStore)));
gtkutil::TextColumn indexCol(_("Index"), COL_INDEX);
gtkutil::TextColumn regexCol(_("Match"), COL_REGEX);
// Create the cell renderer for the action choice
GtkCellRenderer* actionComboRenderer = gtk_cell_renderer_combo_new();
g_object_set(G_OBJECT(actionComboRenderer), "has-entry", FALSE, NULL);
g_object_set(G_OBJECT(actionComboRenderer), "text-column", 1, NULL);
g_object_set(G_OBJECT(actionComboRenderer), "editable", TRUE, NULL);
// Create the store
GtkListStore* actionStore = createActionStore();
g_object_set(G_OBJECT(actionComboRenderer), "model", GTK_TREE_MODEL(actionStore), NULL);
// Construct the column itself
GtkTreeViewColumn* actionCol = gtk_tree_view_column_new_with_attributes(
_("Action"),
actionComboRenderer,
"markup", COL_ACTION,
NULL
);
g_signal_connect(G_OBJECT(actionComboRenderer), "edited", G_CALLBACK(onActionEdited), this);
// Regex editing
GtkCellRendererText* rend = regexCol.getCellRenderer();
g_object_set(G_OBJECT(rend), "editable", TRUE, NULL);
g_signal_connect(G_OBJECT(rend), "edited", G_CALLBACK(onRegexEdited), this);
// Create the cell renderer for the type choice
GtkCellRenderer* typeComboRenderer = gtk_cell_renderer_combo_new();
g_object_set(G_OBJECT(typeComboRenderer), "has-entry", FALSE, NULL);
g_object_set(G_OBJECT(typeComboRenderer), "text-column", 1, NULL);
g_object_set(G_OBJECT(typeComboRenderer), "editable", TRUE, NULL);
// Create the typestore
GtkListStore* typeStore = createTypeStore();
g_object_set(G_OBJECT(typeComboRenderer), "model", GTK_TREE_MODEL(typeStore), NULL);
// Construct the column itself
GtkTreeViewColumn* typeCol = gtk_tree_view_column_new_with_attributes(
_("Type"),
typeComboRenderer,
"markup", COL_TYPE_STR,
NULL
);
g_signal_connect(G_OBJECT(typeComboRenderer), "edited", G_CALLBACK(onTypeEdited), this);
gtk_tree_view_append_column(GTK_TREE_VIEW(_ruleView), indexCol);
gtk_tree_view_append_column(GTK_TREE_VIEW(_ruleView), typeCol);
gtk_tree_view_append_column(GTK_TREE_VIEW(_ruleView), regexCol);
gtk_tree_view_append_column(GTK_TREE_VIEW(_ruleView), actionCol);
GtkTreeSelection* sel = gtk_tree_view_get_selection(GTK_TREE_VIEW(_ruleView));
g_signal_connect(G_OBJECT(sel), "changed", G_CALLBACK(onRuleSelectionChanged), this);
// Action buttons
_widgets[WIDGET_ADD_RULE_BUTTON] = gtk_button_new_from_stock(GTK_STOCK_ADD);
_widgets[WIDGET_MOVE_RULE_UP_BUTTON] = gtk_button_new_from_stock(GTK_STOCK_GO_UP);
_widgets[WIDGET_MOVE_RULE_DOWN_BUTTON] = gtk_button_new_from_stock(GTK_STOCK_GO_DOWN);
_widgets[WIDGET_DELETE_RULE_BUTTON] = gtk_button_new_from_stock(GTK_STOCK_DELETE);
g_signal_connect(G_OBJECT(_widgets[WIDGET_ADD_RULE_BUTTON]), "clicked", G_CALLBACK(onAddRule), this);
g_signal_connect(G_OBJECT(_widgets[WIDGET_MOVE_RULE_UP_BUTTON]), "clicked", G_CALLBACK(onMoveRuleUp), this);
g_signal_connect(G_OBJECT(_widgets[WIDGET_MOVE_RULE_DOWN_BUTTON]), "clicked", G_CALLBACK(onMoveRuleDown), this);
g_signal_connect(G_OBJECT(_widgets[WIDGET_DELETE_RULE_BUTTON]), "clicked", G_CALLBACK(onDeleteRule), this);
GtkWidget* actionVBox = gtk_vbox_new(FALSE, 6);
gtk_box_pack_start(GTK_BOX(actionVBox), _widgets[WIDGET_ADD_RULE_BUTTON], FALSE, FALSE, 0);
gtk_box_pack_start(GTK_BOX(actionVBox), _widgets[WIDGET_MOVE_RULE_UP_BUTTON], FALSE, FALSE, 0);
gtk_box_pack_start(GTK_BOX(actionVBox), _widgets[WIDGET_MOVE_RULE_DOWN_BUTTON], FALSE, FALSE, 0);
gtk_box_pack_start(GTK_BOX(actionVBox), _widgets[WIDGET_DELETE_RULE_BUTTON], FALSE, FALSE, 0);
gtk_box_pack_start(GTK_BOX(hbox), gtkutil::ScrolledFrame(GTK_WIDGET(_ruleView)), TRUE, TRUE, 0);
gtk_box_pack_start(GTK_BOX(hbox), actionVBox, FALSE, FALSE, 0);
return gtkutil::LeftAlignment(hbox, 18, 1);
}
int
MilpRounding::solution(double &solutionValue, double *betterSolution)
{
if(model_->getCurrentPassNumber() > 1) return 0;
if (model_->currentDepth() > 2 && (model_->getNodeCount()%howOften_)!=0)
return 0;
int returnCode = 0; // 0 means it didn't find a feasible solution
OsiTMINLPInterface * nlp = NULL;
if(setup_->getAlgorithm() == B_BB)
nlp = dynamic_cast<OsiTMINLPInterface *>(model_->solver()->clone());
else
nlp = dynamic_cast<OsiTMINLPInterface *>(setup_->nonlinearSolver()->clone());
TMINLP2TNLP* minlp = nlp->problem();
// set tolerances
double integerTolerance = model_->getDblParam(CbcModel::CbcIntegerTolerance);
//double primalTolerance = 1.0e-6;
int n;
int m;
int nnz_jac_g;
int nnz_h_lag;
Ipopt::TNLP::IndexStyleEnum index_style;
minlp->get_nlp_info(n, m, nnz_jac_g,
nnz_h_lag, index_style);
const Bonmin::TMINLP::VariableType* variableType = minlp->var_types();
const double* x_sol = minlp->x_sol();
const double* g_l = minlp->g_l();
const double* g_u = minlp->g_u();
const double * colsol = model_->solver()->getColSolution();
// Get information about the linear and nonlinear part of the instance
TMINLP* tminlp = nlp->model();
vector<Ipopt::TNLP::LinearityType> c_lin(m);
tminlp->get_constraints_linearity(m, c_lin());
vector<int> c_idx(m);
int n_lin = 0;
for (int i=0;i<m;i++) {
if (c_lin[i]==Ipopt::TNLP::LINEAR)
c_idx[i] = n_lin++;
else
c_idx[i] = -1;
}
// Get the structure of the jacobian
vector<int> indexRow(nnz_jac_g);
vector<int> indexCol(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
indexRow(), indexCol(), 0);
// get the jacobian values
vector<double> jac_g(nnz_jac_g);
minlp->eval_jac_g(n, x_sol, false,
m, nnz_jac_g,
NULL, NULL, jac_g());
// Sort the matrix to column ordered
vector<int> sortedIndex(nnz_jac_g);
CoinIotaN(sortedIndex(), nnz_jac_g, 0);
MatComp c;
c.iRow = indexRow();
c.jCol = indexCol();
std::sort(sortedIndex.begin(), sortedIndex.end(), c);
vector<int> row (nnz_jac_g);
vector<double> value (nnz_jac_g);
vector<int> columnStart(n,0);
vector<int> columnLength(n,0);
int indexCorrection = (index_style == Ipopt::TNLP::C_STYLE) ? 0 : 1;
int iniCol = -1;
int nnz = 0;
for(int i=0; i<nnz_jac_g; i++) {
int thisIndexCol = indexCol[sortedIndex[i]]-indexCorrection;
int thisIndexRow = c_idx[indexRow[sortedIndex[i]] - indexCorrection];
if(thisIndexCol != iniCol) {
iniCol = thisIndexCol;
columnStart[thisIndexCol] = nnz;
columnLength[thisIndexCol] = 0;
}
if(thisIndexRow == -1) continue;
columnLength[thisIndexCol]++;
row[nnz] = thisIndexRow;
value[nnz] = jac_g[i];
nnz++;
}
// Build the row lower and upper bounds
vector<double> newRowLower(n_lin);
vector<double> newRowUpper(n_lin);
for(int i = 0 ; i < m ; i++){
if(c_idx[i] == -1) continue;
newRowLower[c_idx[i]] = g_l[i];
newRowUpper[c_idx[i]] = g_u[i];
}
// Get solution array for heuristic solution
vector<double> newSolution(n);
std::copy(x_sol, x_sol + n, newSolution.begin());
// Define the constraint matrix for MILP
CoinPackedMatrix matrix(true,n_lin,n, nnz, value(), row(), columnStart(), columnLength());
// create objective function and columns lower and upper bounds for MILP
// and create columns for matrix in MILP
//double alpha = 0;
double beta = 1;
vector<double> objective(n);
vector<int> idxIntegers;
idxIntegers.reserve(n);
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS){
idxIntegers.push_back(i);
objective[i] = beta*(1 - 2*colsol[i]);
}
}
#if 0
// Get dual multipliers and build gradient of the lagrangean
const double * duals = nlp->getRowPrice() + 2 *n;
vector<double> grad(n, 0);
vector<int> indices(n, 0);
tminlp->eval_grad_f(n, x_sol, false, grad());
for(int i = 0 ; i < m ; i++){
if(c_lin[i] == Ipopt::TNLP::LINEAR) continue;
int nnz;
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, indices(), NULL);
tminlp->eval_grad_gi(n, x_sol, false, i, nnz, NULL, grad());
for(int k = 0 ; k < nnz ; k++){
objective[indices[k]] += alpha *duals[i] * grad[k];
}
}
for(int i = 0 ; i < n ; i++){
if(variableType[i] != Bonmin::TMINLP::CONTINUOUS)
objective[i] += alpha * grad[i];
//if(fabs(objective[i]) < 1e-4) objective[i] = 0;
else objective[i] = 0;
}
std::copy(objective.begin(), objective.end(), std::ostream_iterator<double>(std::cout, " "));
std::cout<<std::endl;
#endif
// load the problem to OSI
OsiSolverInterface *si = mip_->solver();
assert(si != NULL);
CoinMessageHandler * handler = model_->messageHandler()->clone();
si->passInMessageHandler(handler);
si->messageHandler()->setLogLevel(1);
si->loadProblem(matrix, model_->solver()->getColLower(), model_->solver()->getColUpper(), objective(),
newRowLower(), newRowUpper());
si->setInteger(idxIntegers(), static_cast<int>(idxIntegers.size()));
si->applyCuts(noGoods);
bool hasFractionnal = true;
while(hasFractionnal){
mip_->optimize(DBL_MAX, 0, 60);
hasFractionnal = false;
#if 0
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
cout<<"It should be infeasible because: "<<endl;
cout<<"variable "<<iColumn<<" is not integer"<<endl;
#endif
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
}
#endif
}
bool feasible = false;
if(mip_->getLastSolution()) {
const double* solution = mip_->getLastSolution();
std::copy(solution, solution + n, newSolution.begin());
feasible = true;
delete handler;
}
if(feasible) {
// fix the integer variables and solve the NLP
// also add no good cut
CoinPackedVector v;
double lb = 1;
for (int iColumn=0;iColumn<n;iColumn++) {
if (variableType[iColumn] != Bonmin::TMINLP::CONTINUOUS) {
double value=newSolution[iColumn];
if (fabs(floor(value+0.5)-value)>integerTolerance) {
feasible = false;
break;
}
else {
value=floor(newSolution[iColumn]+0.5);
if(value > 0.5){
v.insert(iColumn, -1);
lb -= value;
}
minlp->SetVariableUpperBound(iColumn, value);
minlp->SetVariableLowerBound(iColumn, value);
}
}
}
OsiRowCut c;
c.setRow(v);
c.setLb(lb);
c.setUb(DBL_MAX);
noGoods.insert(c);
if(feasible) {
nlp->initialSolve();
if(minlp->optimization_status() != Ipopt::SUCCESS) {
feasible = false;
}
std::copy(x_sol,x_sol+n, newSolution.begin());
}
}
if(feasible) {
double newSolutionValue;
minlp->eval_f(n, newSolution(), true, newSolutionValue);
if(newSolutionValue < solutionValue) {
std::copy(newSolution.begin(), newSolution.end(), betterSolution);
solutionValue = newSolutionValue;
returnCode = 1;
}
}
delete nlp;
#ifdef DEBUG_BON_HEURISTIC_DIVE_MIP
std::cout<<"DiveMIP returnCode = "<<returnCode<<std::endl;
#endif
return returnCode;
}
