/** Solves a standard form LP problem in the form of min(c) s.t. A.x = b lb <= x <= ub */
protected int optimizeStandardLP(int nOfSlackVariables) throws Exception {
log.info("optimizeStandardLP");
LPOptimizationRequest lpRequest = getLPOptimizationRequest();
if (log.isDebugEnabled() && lpRequest.isDumpProblem()) {
log.debug("LP problem: " + lpRequest.toString());
}
LPOptimizationResponse lpResponse;
if (lpRequest.isPresolvingDisabled()) {
// optimization
LPPrimalDualMethod opt = new LPPrimalDualMethod(minLBValue, maxUBValue);
opt.setLPOptimizationRequest(lpRequest);
if (opt.optimizePresolvedStandardLP() == OptimizationResponse.FAILED) {
return OptimizationResponse.FAILED;
}
lpResponse = opt.getLPOptimizationResponse();
setLPOptimizationResponse(lpResponse);
} else {
// presolving
LPPresolver lpPresolver = new LPPresolver();
lpPresolver.setAvoidScaling(lpRequest.isRescalingDisabled());
lpPresolver.setAvoidFillIn(lpRequest.isAvoidPresolvingFillIn());
lpPresolver.setAvoidIncreaseSparsity(lpRequest.isAvoidPresolvingIncreaseSparsity());
testPresolver = lpPresolver; // just for testing
lpPresolver.setNOfSlackVariables((short) nOfSlackVariables);
lpPresolver.presolve(getC(), getA(), getB(), getLb(), getUb());
int presolvedDim = lpPresolver.getPresolvedN();
if (presolvedDim == 0) {
// deterministic problem
log.debug("presolvedDim : " + presolvedDim);
log.debug("deterministic LP problem");
lpResponse = new LPOptimizationResponse();
lpResponse.setReturnCode(OptimizationResponse.SUCCESS);
lpResponse.setSolution(new double[] {});
} else {
// solving the presolved problem
DoubleMatrix1D presolvedC = lpPresolver.getPresolvedC();
DoubleMatrix2D presolvedA = lpPresolver.getPresolvedA();
DoubleMatrix1D presolvedB = lpPresolver.getPresolvedB();
if (log.isDebugEnabled()) {
if (lpPresolver.getPresolvedYlb() != null) {
log.debug("Ylb: " + ArrayUtils.toString(lpPresolver.getPresolvedYlb().toArray()));
log.debug("Yub: " + ArrayUtils.toString(lpPresolver.getPresolvedYub().toArray()));
}
if (lpPresolver.getPresolvedZlb() != null) {
log.debug("Zlb: " + ArrayUtils.toString(lpPresolver.getPresolvedZlb().toArray()));
log.debug("Zub: " + ArrayUtils.toString(lpPresolver.getPresolvedZub().toArray()));
}
}
// new LP problem (the presolved problem)
LPOptimizationRequest presolvedLPRequest = lpRequest.cloneMe();
presolvedLPRequest.setC(presolvedC);
presolvedLPRequest.setA(presolvedA);
presolvedLPRequest.setB(presolvedB);
presolvedLPRequest.setLb(lpPresolver.getPresolvedLB());
presolvedLPRequest.setUb(lpPresolver.getPresolvedUB());
presolvedLPRequest.setYlb(lpPresolver.getPresolvedYlb());
presolvedLPRequest.setYub(lpPresolver.getPresolvedYub());
presolvedLPRequest.setZlb(lpPresolver.getPresolvedZlb());
presolvedLPRequest.setZub(lpPresolver.getPresolvedZub());
if (getInitialPoint() != null) {
presolvedLPRequest.setInitialPoint(lpPresolver.presolve(getInitialPoint().toArray()));
}
if (getNotFeasibleInitialPoint() != null) {
presolvedLPRequest.setNotFeasibleInitialPoint(
lpPresolver.presolve(getNotFeasibleInitialPoint().toArray()));
}
// optimization
// NB: because of rescaling during the presolving phase, minLB and maxUB could have been
// rescaled
double rescaledMinLBValue =
(Double.isNaN(lpPresolver.getMinRescaledLB()))
? this.minLBValue
: lpPresolver.getMinRescaledLB();
double rescaledMaxUBValue =
(Double.isNaN(lpPresolver.getMaxRescaledUB()))
? this.maxUBValue
: lpPresolver.getMaxRescaledUB();
LPPrimalDualMethod opt = new LPPrimalDualMethod(rescaledMinLBValue, rescaledMaxUBValue);
opt.setLPOptimizationRequest(presolvedLPRequest);
if (opt.optimizePresolvedStandardLP() == OptimizationResponse.FAILED) {
return OptimizationResponse.FAILED;
}
lpResponse = opt.getLPOptimizationResponse();
}
// postsolving
double[] postsolvedSolution = lpPresolver.postsolve(lpResponse.getSolution());
lpResponse.setSolution(postsolvedSolution);
setLPOptimizationResponse(lpResponse);
}
return lpResponse.getReturnCode();
}
