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some more dual presolve on exactly_one, at_most one in CP-SAT; minor cleaning

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Laurent Perron
2021-02-19 10:58:09 +01:00
parent 48303577e8
commit 247de5ae2a
15 changed files with 275 additions and 1164 deletions
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120
ortools/sat/samples/MultipleKnapsackSat.java
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@@ -69,72 +69,68 @@ public class MultipleKnapsackSat {
totalValue = totalValue + data.values[i];
}
try {
// [START model]
CpModel model = new CpModel();
// [END model]
// [START model]
CpModel model = new CpModel();
// [END model]
// [START variables]
IntVar[][] x = new IntVar[data.numItems][data.numBins];
for (int i = 0; i < data.numItems; ++i) {
for (int b = 0; b < data.numBins; ++b) {
x[i][b] = model.newIntVar(0, 1, "x_" + i + "_" + b);
}
}
// Main variables.
// Load and value variables.
IntVar[] load = new IntVar[data.numBins];
IntVar[] value = new IntVar[data.numBins];
// [START variables]
IntVar[][] x = new IntVar[data.numItems][data.numBins];
for (int i = 0; i < data.numItems; ++i) {
for (int b = 0; b < data.numBins; ++b) {
load[b] = model.newIntVar(0, data.binCapacities[b], "load_" + b);
value[b] = model.newIntVar(0, totalValue, "value_" + b);
x[i][b] = model.newIntVar(0, 1, "x_" + i + "_" + b);
}
// Links load and value with x.
int[] sizes = new int[data.numItems];
for (int i = 0; i < data.numItems; ++i) {
sizes[i] = data.items[i];
}
for (int b = 0; b < data.numBins; ++b) {
IntVar[] vars = new IntVar[data.numItems];
for (int i = 0; i < data.numItems; ++i) {
vars[i] = x[i][b];
}
model.addEquality(LinearExpr.scalProd(vars, data.items), load[b]);
model.addEquality(LinearExpr.scalProd(vars, data.values), value[b]);
}
// [END variables]
// [START constraints]
// Each item can be in at most one bin.
// Place all items.
for (int i = 0; i < data.numItems; ++i) {
IntVar[] vars = new IntVar[data.numBins];
for (int b = 0; b < data.numBins; ++b) {
vars[b] = x[i][b];
}
model.addLessOrEqual(LinearExpr.sum(vars), 1);
}
// [END constraints]
// Maximize sum of load.
// [START objective]
model.maximize(LinearExpr.sum(value));
// [END objective]
// [START solve]
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.solve(model);
// [END solve]
// [START print_solution]
System.out.println("Solve status: " + status);
if (status == CpSolverStatus.OPTIMAL) {
printSolution(data, solver, x, load, value);
}
// [END print_solution]
} catch (Exception e) {
System.err.println("Caught " + e + " while building the model");
}
// Main variables.
// Load and value variables.
IntVar[] load = new IntVar[data.numBins];
IntVar[] value = new IntVar[data.numBins];
for (int b = 0; b < data.numBins; ++b) {
load[b] = model.newIntVar(0, data.binCapacities[b], "load_" + b);
value[b] = model.newIntVar(0, totalValue, "value_" + b);
}
// Links load and value with x.
int[] sizes = new int[data.numItems];
for (int i = 0; i < data.numItems; ++i) {
sizes[i] = data.items[i];
}
for (int b = 0; b < data.numBins; ++b) {
IntVar[] vars = new IntVar[data.numItems];
for (int i = 0; i < data.numItems; ++i) {
vars[i] = x[i][b];
}
model.addEquality(LinearExpr.scalProd(vars, data.items), load[b]);
model.addEquality(LinearExpr.scalProd(vars, data.values), value[b]);
}
// [END variables]
// [START constraints]
// Each item can be in at most one bin.
// Place all items.
for (int i = 0; i < data.numItems; ++i) {
IntVar[] vars = new IntVar[data.numBins];
for (int b = 0; b < data.numBins; ++b) {
vars[b] = x[i][b];
}
model.addLessOrEqual(LinearExpr.sum(vars), 1);
}
// [END constraints]
// Maximize sum of load.
// [START objective]
model.maximize(LinearExpr.sum(value));
// [END objective]
// [START solve]
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.solve(model);
// [END solve]
// [START print_solution]
System.out.println("Solve status: " + status);
if (status == CpSolverStatus.OPTIMAL) {
printSolution(data, solver, x, load, value);
}
// [END print_solution]
}
private MultipleKnapsackSat() {}