static void attempt5(final boolean identity, final boolean addDummyConstraints) {
final ConvexSolver cs = P20150809.buildModel(identity, addDummyConstraints);
try {
final Optimisation.Result solution = cs.solve();
if ((solution.getState() == Optimisation.State.DISTINCT)
|| (solution.getState() == Optimisation.State.APPROXIMATE)
|| (solution.getState() == Optimisation.State.OPTIMAL)) {
final double[] pt = new double[4];
for (int i = 0; i < pt.length; i++) {
pt[i] = solution.doubleValue(i);
}
System.out.println("Objective " + solution.getValue());
for (int ii = 0; ii < 4; ii++) {
System.out.println("x[" + ii + "] = " + solution.doubleValue(ii));
}
} else {
System.out.println("Failure State = " + solution.getState().name());
}
} catch (final Exception e) {
System.out.println(e);
}
}
static ConvexSolver buildModel(final boolean identity, final boolean addDummyConstraints) {
if (!identity && !addDummyConstraints) {
System.out.println("Zero Q matrix and no constraints -------------------------!");
} else if (!identity) {
System.out.println("Zero Q matrix and constraints -------------------------!");
} else if (!addDummyConstraints) {
System.out.println("Identity Q matrix and no constraints -------------------------!");
} else {
System.out.println("Identity Q matrix and constraints -------------------------!");
}
final double[] C = new double[] {0.12, -0.05, 0.08, 0.07};
final RawStore cov = new RawStore(4, 4);
if (identity) {
for (int i = 0; i < 4; i++) {
cov.set(i, i, 1.0);
}
}
final RawStore linPart = new RawStore(C, 4);
ConvexSolver.Builder builder = ConvexSolver.getBuilder(cov, linPart);
if (addDummyConstraints) {
final RawStore ineq =
RawStore.FACTORY.rows(
new double[][] {
{-1.0, 0.0, 0.0, 0.0},
{0.0, -1.0, 0.0, 0.0},
{0.0, 0.0, -1.0, 0.0},
{0.0, 0.0, 0.0, -1.0},
{1.0, 0.0, 0.0, 0.0},
{0.0, 1.0, 0.0, 0.0},
{0.0, 0.0, 1.0, 0.0},
{0.0, 0.0, 0.0, 1.0}
});
final RawStore coeff =
RawStore.FACTORY.columns(
new double[][] {{99999, 99999, 99999, 99999, 99999, 99999, 99999, 99999}});
builder = builder.inequalities(ineq, coeff);
}
final Optimisation.Options opts = new Optimisation.Options();
opts.iterations_abort = 10000;
opts.iterations_suffice = 100;
if (OptimisationConvexTests.DEBUG) {
opts.debug(ConvexSolver.class);
}
return builder.build(opts);
}
