private MaxSATResult none() {
nbInitialVariables = nVars();
Tristate res;
this.initRelaxation();
this.solver = this.rebuildSolver();
LNGIntVector assumptions = new LNGIntVector();
LNGIntVector currentObjFunction = new LNGIntVector();
this.encoder.setIncremental(IncrementalStrategy.NONE);
this.activeSoft.growTo(nSoft(), false);
for (int i = 0; i < nSoft(); i++) this.coreMapping.put(softClauses.get(i).assumptionVar(), i);
while (true) {
res = searchSATSolver(this.solver, satHandler(), assumptions);
if (res == Tristate.UNDEF) return MaxSATResult.UNDEF;
else if (res == Tristate.TRUE) {
nbSatisfiable++;
int newCost = computeCostModel(this.solver.model(), Integer.MAX_VALUE);
saveModel(this.solver.model());
if (verbosity != Verbosity.NONE) this.output.println("o " + newCost);
ubCost = newCost;
if (nbSatisfiable == 1) {
if (!foundUpperBound(ubCost, null)) return MaxSATResult.UNDEF;
for (int i = 0; i < this.objFunction.size(); i++)
assumptions.push(not(this.objFunction.get(i)));
} else return MaxSATResult.OPTIMUM;
} else {
lbCost++;
nbCores++;
if (verbosity != Verbosity.NONE) this.output.println("c LB : " + lbCost);
if (nbSatisfiable == 0) return MaxSATResult.UNSATISFIABLE;
else if (lbCost == ubCost) {
assert nbSatisfiable > 0;
if (verbosity != Verbosity.NONE) this.output.println("c LB = UB");
return MaxSATResult.OPTIMUM;
} else if (!foundLowerBound(lbCost, null)) return MaxSATResult.UNDEF;
sumSizeCores += this.solver.conflict().size();
for (int i = 0; i < this.solver.conflict().size(); i++) {
assert !this.activeSoft.get(this.coreMapping.get(this.solver.conflict().get(i)));
this.activeSoft.set(this.coreMapping.get(this.solver.conflict().get(i)), true);
}
currentObjFunction.clear();
assumptions.clear();
for (int i = 0; i < nSoft(); i++) {
if (this.activeSoft.get(i))
currentObjFunction.push(softClauses.get(i).relaxationVars().get(0));
else assumptions.push(not(softClauses.get(i).assumptionVar()));
}
if (verbosity != Verbosity.NONE)
this.output.println(
String.format(
"c Relaxed soft clauses %d / %d",
currentObjFunction.size(), this.objFunction.size()));
this.solver = this.rebuildSolver();
this.encoder.encodeCardinality(this.solver, currentObjFunction, lbCost);
}
}
}
private MaxSATResult iterative() {
if (this.encoder.cardEncoding() != CardinalityEncoding.TOTALIZER)
throw new IllegalStateException(
"Error: Currently algorithm MSU3 with iterative encoding only supports the totalizer encoding.");
nbInitialVariables = nVars();
Tristate res;
this.initRelaxation();
this.solver = this.rebuildSolver();
final LNGIntVector assumptions = new LNGIntVector();
final LNGIntVector joinObjFunction = new LNGIntVector();
final LNGIntVector currentObjFunction = new LNGIntVector();
final LNGIntVector encodingAssumptions = new LNGIntVector();
this.encoder.setIncremental(IncrementalStrategy.ITERATIVE);
this.activeSoft.growTo(nSoft(), false);
for (int i = 0; i < nSoft(); i++) this.coreMapping.put(softClauses.get(i).assumptionVar(), i);
while (true) {
res = searchSATSolver(this.solver, satHandler(), assumptions);
if (res == Tristate.UNDEF) return MaxSATResult.UNDEF;
else if (res == Tristate.TRUE) {
nbSatisfiable++;
int newCost = computeCostModel(this.solver.model(), Integer.MAX_VALUE);
saveModel(this.solver.model());
if (verbosity != Verbosity.NONE) this.output.println("o " + newCost);
ubCost = newCost;
if (nbSatisfiable == 1) {
if (!foundUpperBound(ubCost, null)) return MaxSATResult.UNDEF;
for (int i = 0; i < this.objFunction.size(); i++)
assumptions.push(not(this.objFunction.get(i)));
} else {
assert lbCost == newCost;
return MaxSATResult.OPTIMUM;
}
} else {
lbCost++;
nbCores++;
if (verbosity != Verbosity.NONE) this.output.println("c LB : " + lbCost);
if (nbSatisfiable == 0) return MaxSATResult.UNSATISFIABLE;
if (lbCost == ubCost) {
assert nbSatisfiable > 0;
if (verbosity != Verbosity.NONE) this.output.println("c LB = UB");
return MaxSATResult.OPTIMUM;
}
sumSizeCores += this.solver.conflict().size();
if (this.solver.conflict().size() == 0) return MaxSATResult.UNSATISFIABLE;
if (!foundLowerBound(lbCost, null)) return MaxSATResult.UNDEF;
joinObjFunction.clear();
for (int i = 0; i < this.solver.conflict().size(); i++) {
if (this.coreMapping.containsKey(this.solver.conflict().get(i))) {
assert !this.activeSoft.get(this.coreMapping.get(this.solver.conflict().get(i)));
this.activeSoft.set(this.coreMapping.get(this.solver.conflict().get(i)), true);
joinObjFunction.push(
softClauses
.get(this.coreMapping.get(this.solver.conflict().get(i)))
.relaxationVars()
.get(0));
}
}
currentObjFunction.clear();
assumptions.clear();
for (int i = 0; i < nSoft(); i++)
if (this.activeSoft.get(i))
currentObjFunction.push(softClauses.get(i).relaxationVars().get(0));
else assumptions.push(not(softClauses.get(i).assumptionVar()));
if (verbosity != Verbosity.NONE)
this.output.println(
String.format(
"c Relaxed soft clauses %d / %d",
currentObjFunction.size(), this.objFunction.size()));
if (!this.encoder.hasCardEncoding()) {
if (lbCost != currentObjFunction.size()) {
this.encoder.buildCardinality(this.solver, currentObjFunction, lbCost);
joinObjFunction.clear();
this.encoder.incUpdateCardinality(
this.solver, joinObjFunction, currentObjFunction, lbCost, encodingAssumptions);
}
} else
this.encoder.incUpdateCardinality(
this.solver, joinObjFunction, currentObjFunction, lbCost, encodingAssumptions);
for (int i = 0; i < encodingAssumptions.size(); i++)
assumptions.push(encodingAssumptions.get(i));
}
}
}