Examples with State org.ojalgo.optimisation.Optimisation.State used on opensource projects

Example 1 with State

use of org.ojalgo.optimisation.Optimisation.State in project ojAlgo-finance by optimatika.

the class GitHubIssue24 method testSuccess.

@Test
public void testSuccess() throws Exception {
    final MarkowitzModel markowitzModel = GitHubIssue24.buildMarkowitzModel(0.015, false, false, false);
    final double tmpMeanReturn = markowitzModel.getMeanReturn();
    if (DEBUG) {
        BasicLogger.debug(tmpMeanReturn);
    }
    final State tmpOptimisationState = markowitzModel.optimiser().getState();
    TestUtils.assertTrue(tmpOptimisationState.isOptimal());
}

Example 2 with State

use of org.ojalgo.optimisation.Optimisation.State in project ojAlgo-finance by optimatika.

the class PortfolioProblems method testP20130329.

/**
 * we have a problem with MarkowitzModel. I have produced a little source that explain the problem. We
 * have set 3 different TargetReturn on the same data.. With targets near to the best end worst target
 * return, the MarkowitzModel works fine. With targets within the interval of best and worst return, seem
 * that the MarkowitzModel is not able to find a correct list of weights. If you try this program and use
 * target of 0.08 or 0.13 or 0.12 you can see a correct solution. With a target of 0.10 MarkowitzModel is
 * not able to find a valid solution.
 */
@Test
public void testP20130329() {
    final BasicMatrix tmpCovariances = RationalMatrix.FACTORY.rows(new double[][] { { 0.00360000, 0.001800000000 }, { 0.001800000000, 0.00090000 } });
    // final Eigenvalue<Double> tmpEvD = Eigenvalue.makePrimitive(true);
    // tmpEvD.compute(tmpCovariances, true);
    // BasicLogger.debug("Eigenvalues: {}", tmpEvD.getEigenvalues());
    final MarketEquilibrium tmpMarketEquilibrium = new MarketEquilibrium(tmpCovariances, BigMath.THOUSAND);
    final Builder<RationalMatrix> tmpExcessReturnsBuilder = RationalMatrix.FACTORY.getBuilder(2, 1);
    tmpExcessReturnsBuilder.set(0, 0, 0.1400);
    tmpExcessReturnsBuilder.set(1, 0, 0.0800);
    final BasicMatrix tmpExcessReturns = tmpExcessReturnsBuilder.build();
    final MarkowitzModel tmpMarkowitzModel = new MarkowitzModel(tmpMarketEquilibrium, tmpExcessReturns);
    tmpMarkowitzModel.setLowerLimit(0, BigMath.ZERO);
    tmpMarkowitzModel.setUpperLimit(0, BigMath.ONE);
    tmpMarkowitzModel.setLowerLimit(1, BigMath.ZERO);
    tmpMarkowitzModel.setUpperLimit(1, BigMath.ONE);
    tmpMarkowitzModel.setShortingAllowed(false);
    for (int t = 8; t <= 14; t++) {
        final BigDecimal tmpTargetReturn = BigFunction.DIVIDE.invoke(new BigDecimal(t), BigMath.HUNDRED);
        tmpMarkowitzModel.setTargetReturn(tmpTargetReturn);
        final List<BigDecimal> tmpWeights = tmpMarkowitzModel.getWeights();
        final State tmpOptimisationState = tmpMarkowitzModel.optimiser().getState();
        if (DEBUG) {
            BasicLogger.debug("State {} {}", tmpOptimisationState, tmpWeights);
        }
        TestUtils.assertTrue("Optimisation State", tmpOptimisationState.isOptimal());
        TestUtils.assertTrue("Asset0 >= 0.0", tmpWeights.get(0).signum() >= 0);
        TestUtils.assertTrue("Asset1 >= 0.0", tmpWeights.get(1).signum() >= 0);
        TestUtils.assertTrue("Asset0 <= 1.0", tmpWeights.get(0).compareTo(BigMath.ONE) <= 0);
        TestUtils.assertTrue("Asset1 <= 1.0", tmpWeights.get(1).compareTo(BigMath.ONE) <= 0);
        TestUtils.assertEquals("Asset0 + Asset1 == 1.0", 1.0, tmpWeights.get(0).add(tmpWeights.get(1)).doubleValue(), 0.0001);
    }
}

Example 3 with State

use of org.ojalgo.optimisation.Optimisation.State in project ojAlgo by optimatika.

the class SpecificBranchCase method testVpm2FirstBranch.

@Test
public void testVpm2FirstBranch() {
    final File tmpFile = new File(MipLibCase.PATH + "vpm2.mps");
    final MathProgSysModel tmpMPS = MathProgSysModel.make(tmpFile);
    final ExpressionsBasedModel tmpModel = tmpMPS.getExpressionsBasedModel();
    TestUtils.assertTrue(tmpModel.validate());
    final ExpressionsBasedModel tmpLowerBranchModel = tmpModel.relax(false);
    final ExpressionsBasedModel tmpUpperBranchModel = tmpModel.relax(false);
    tmpLowerBranchModel.getVariable(106).upper(BigMath.ZERO);
    tmpUpperBranchModel.getVariable(106).lower(BigMath.ONE);
    final Optimisation.Result tmpLowerResult = tmpLowerBranchModel.minimise();
    final Optimisation.Result tmpUpperResult = tmpUpperBranchModel.minimise();
    final State tmpLowerState = tmpLowerResult.getState();
    final State tmpUpperState = tmpUpperResult.getState();
    if (!tmpLowerState.isFeasible() && !tmpUpperState.isFeasible()) {
        TestUtils.fail("Both these branches cannot be infeasible!");
    }
    tmpLowerBranchModel.minimise();
    if (tmpLowerState.isFeasible() && !tmpLowerBranchModel.validate(MipLibCase.PRECISION)) {
        TestUtils.fail(MipLibCase.SOLUTION_NOT_VALID);
    }
    tmpUpperBranchModel.minimise();
    if (tmpUpperState.isFeasible() && !tmpUpperBranchModel.validate(MipLibCase.PRECISION)) {
        TestUtils.fail(MipLibCase.SOLUTION_NOT_VALID);
    }
}

Example 4 with State

use of org.ojalgo.optimisation.Optimisation.State in project ojAlgo by optimatika.

the class LinearProblems method testP20100412.

/**
 * Didn't recognise this as an infeasible problem.
 */
@Test
public void testP20100412() {
    final ExpressionsBasedModel tmpModel = OptimisationIntegerData.buildModelForP20100412().relax(true);
    // tmpModel.relax(); // Relax the integer constraints
    // Set branch state
    tmpModel.getVariable(1).lower(ONE);
    final State tmpResultState = tmpModel.maximise().getState();
    TestUtils.assertFalse("Should be INFEASIBLE", tmpResultState == State.FEASIBLE);
}