Examples with ExpressionsBasedModel org.ojalgo.optimisation.ExpressionsBasedModel used on opensource projects

Example 1 with ExpressionsBasedModel

use of org.ojalgo.optimisation.ExpressionsBasedModel in project ojAlgo by optimatika.

the class NewIntegerSolver method makeNodeModel.

ExpressionsBasedModel makeNodeModel(final NodeKey nodeKey) {
    final ExpressionsBasedModel retVal = this.getIntegerModel().relax(false);
    final int[] tmpIntegerIndeces = this.getIntegerIndices();
    for (int i = 0; i < tmpIntegerIndeces.length; i++) {
        final BigDecimal tmpLowerBound = nodeKey.getLowerBound(i);
        final BigDecimal tmpUpperBound = nodeKey.getUpperBound(i);
        final Variable tmpVariable = retVal.getVariable(tmpIntegerIndeces[i]);
        tmpVariable.lower(tmpLowerBound);
        tmpVariable.upper(tmpUpperBound);
        BigDecimal tmpValue = tmpVariable.getValue();
        if (tmpValue != null) {
            if (tmpLowerBound != null) {
                tmpValue = tmpValue.max(tmpLowerBound);
            }
            if (tmpUpperBound != null) {
                tmpValue = tmpValue.min(tmpUpperBound);
            }
            tmpVariable.setValue(tmpValue);
        }
    }
    if (this.isIntegerSolutionFound()) {
        final double tmpBestValue = this.getBestResultSoFar().getValue();
        final double tmpGap = PrimitiveFunction.ABS.invoke(tmpBestValue * options.mip_gap);
        if (retVal.isMinimisation()) {
            retVal.limitObjective(null, TypeUtils.toBigDecimal(tmpBestValue - tmpGap, options.feasibility));
        } else {
            retVal.limitObjective(TypeUtils.toBigDecimal(tmpBestValue + tmpGap, options.feasibility), null);
        }
    }
    return retVal;
}

Example 2 with ExpressionsBasedModel

use of org.ojalgo.optimisation.ExpressionsBasedModel in project ojAlgo by optimatika.

the class ComPictetPamBamTest method setupModel.

/**
 * @param numberOfVars greater than 6
 */
void setupModel(final int numberOfVars) {
    if (numberOfVars < 6) {
        throw new IllegalArgumentException("numberOfVars must be >= 6 !!!");
    }
    // 
    // variables
    // 
    vars = new Variable[numberOfVars];
    vars[0] = new Variable("x0").lower(new BigDecimal(00.0)).upper(new BigDecimal(15.0));
    vars[1] = new Variable("x1").lower(new BigDecimal(17.0)).upper(new BigDecimal(27.0));
    vars[2] = new Variable("x2").lower(new BigDecimal(19.0)).upper(new BigDecimal(34.0));
    vars[3] = new Variable("x3").lower(new BigDecimal(25.0)).upper(new BigDecimal(48.0));
    vars[4] = new Variable("x4").lower(new BigDecimal(05.0)).upper(new BigDecimal(18.0));
    vars[5] = new Variable("x5").lower(new BigDecimal(02.0)).upper(new BigDecimal(09.0));
    for (int i = 6; i < numberOfVars; ++i) {
        vars[i] = new Variable("x" + i).level(BigMath.ZERO);
    }
    // 
    // minimise distance to this point
    // 
    point = new BigDecimal[numberOfVars];
    point[0] = new BigDecimal(1.0);
    point[1] = new BigDecimal(25.0);
    point[2] = new BigDecimal(33.0);
    point[3] = new BigDecimal(29.0);
    point[4] = new BigDecimal(9.0);
    point[5] = new BigDecimal(2.0);
    for (int i = 6; i < numberOfVars; ++i) {
        point[i] = new BigDecimal(0.0);
    }
    // 
    // model
    // 
    model = new ExpressionsBasedModel(vars);
    // 
    // objective function
    // 
    {
        final int tmpLength = model.countVariables();
        final Expression retVal = model.addExpression("objective");
        for (int ij = 0; ij < tmpLength; ij++) {
            retVal.set(ij, ij, ONE);
        }
        final BigDecimal tmpLinearWeight = TWO.negate();
        for (int i = 0; i < tmpLength; i++) {
            retVal.set(i, Arrays.asList(point).get(i).multiply(tmpLinearWeight));
        }
        final Expression e = retVal;
        e.weight(BigMath.HALF);
    }
    // 
    // sum(xi) = 100.0
    // 
    {
        final int tmpLength = model.countVariables();
        final Expression retVal = model.addExpression("sum(xi) = 100.0");
        for (int i = 0; i < tmpLength; i++) {
            retVal.set(i, ONE);
        }
        final Expression e = retVal;
        e.level(BigMath.HUNDRED);
    }
    // 
    // x1 + x2 <= 45
    // 
    {
        final Expression e = model.addExpression("x1 + x2 <= 45.0");
        e.set(1, BigMath.ONE);
        e.set(2, BigMath.ONE);
        e.lower(BigMath.ZERO).upper(new BigDecimal(45.0));
    }
    // 
    // x4 - 2*x5 = 0
    // 
    {
        final Expression e = model.addExpression("x4 - 2*x5 = 0");
        e.set(4, BigMath.ONE);
        e.set(5, BigMath.TWO.negate());
        e.level(BigMath.ZERO);
    }
// model.options.debug(ConvexSolver.class);
}

Example 3 with ExpressionsBasedModel

use of org.ojalgo.optimisation.ExpressionsBasedModel in project ojAlgo by optimatika.

the class ConvexProblems method testP20150720.

/**
 * Issue reported at GitHub
 * <p>
 * apete: I believe there are problems with the models the user supplied, but ojAlgo fails to correctly
 * identify and report these problems. Instead ojAlgo struggles and returns different solutions with
 * sequential executions. This test is designed to (only) ensure consistency between exections. (I don't
 * know what the correct solution is.)
 * </p>
 *
 * @see <a href="https://github.com/optimatika/ojAlgo/issues/5">GitHub Issue 5</a>
 */
@Test
public void testP20150720() {
    final ExpressionsBasedModel tmpModel1 = P20150720.buildModel1();
    final ExpressionsBasedModel tmpModel2 = P20150720.buildModel2();
    final ExpressionsBasedModel tmpModel3 = P20150720.buildModel3();
    // The problem is with the ConvexSolver, and it is present without integer constraints
    tmpModel1.relax(true);
    tmpModel2.relax(true);
    tmpModel3.relax(true);
    final Result tmpBaseResult1 = tmpModel1.maximise();
    final Result tmpBaseResult2 = tmpModel2.maximise();
    final Result tmpBaseResult3 = tmpModel3.maximise();
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpModel1, null);
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpModel2, null);
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpModel3, null);
    for (int l = 0; l < 10; l++) {
        final Result tmpResult1 = tmpModel1.maximise();
        if (OptimisationConvexTests.DEBUG) {
            BasicLogger.debug();
            BasicLogger.debug("Model 1");
            BasicLogger.debug(tmpResult1);
            BasicLogger.debug(tmpModel1);
        }
        TestUtils.assertStateNotLessThanFeasible(tmpResult1);
        TestUtils.assertEquals("Model 1 State @" + l, tmpBaseResult1.getState(), tmpResult1.getState());
        TestUtils.assertEquals("Model 1 Value @" + l, tmpBaseResult1.getValue(), tmpResult1.getValue());
        TestUtils.assertEquals("Model 1 Solution @" + l, tmpBaseResult1, tmpResult1);
        final Result tmpResult2 = tmpModel2.maximise();
        if (OptimisationConvexTests.DEBUG) {
            BasicLogger.debug();
            BasicLogger.debug("Model 2");
            BasicLogger.debug(tmpResult2);
            BasicLogger.debug(tmpModel2);
        }
        TestUtils.assertStateNotLessThanFeasible(tmpResult2);
        TestUtils.assertEquals("Model 2 State @" + l, tmpBaseResult2.getState(), tmpResult2.getState());
        TestUtils.assertEquals("Model 2 Value @" + l, tmpBaseResult2.getValue(), tmpResult2.getValue());
        TestUtils.assertEquals("Model 2 Solution @" + l, tmpBaseResult2, tmpResult2);
        final Result tmpResult3 = tmpModel3.maximise();
        if (OptimisationConvexTests.DEBUG) {
            BasicLogger.debug();
            BasicLogger.debug("Model 3");
            BasicLogger.debug(tmpResult3);
            BasicLogger.debug(tmpModel3);
        }
        TestUtils.assertStateNotLessThanFeasible(tmpResult3);
        TestUtils.assertEquals("Model 3 State @" + l, tmpBaseResult3.getState(), tmpResult3.getState());
        TestUtils.assertEquals("Model 3 Value @" + l, tmpBaseResult3.getValue(), tmpResult3.getValue());
        TestUtils.assertEquals("Model 3 Solution @" + l, tmpBaseResult3, tmpResult3);
    }
}

Example 4 with ExpressionsBasedModel

use of org.ojalgo.optimisation.ExpressionsBasedModel in project ojAlgo by optimatika.

the class ConvexProblems method doEarly2008.

static void doEarly2008(final Variable[] variables, final Access2D<?> covariances, final Access1D<?> expected) {
    final ExpressionsBasedModel tmpModel = new ExpressionsBasedModel(variables);
    final Expression tmpVariance = tmpModel.addExpression("Variance");
    tmpVariance.setQuadraticFactors(tmpModel.getVariables(), covariances);
    tmpVariance.weight(BigMath.PI.multiply(BigMath.E).multiply(BigMath.HALF));
    final Expression tmpBalance = tmpModel.addExpression("Balance");
    tmpBalance.setLinearFactorsSimple(tmpModel.getVariables());
    tmpBalance.level(BigMath.ONE);
    // tmpModel.options.debug(ConvexSolver.class);
    // tmpModel.options.validate = false;
    final Result tmpActualResult = tmpModel.minimise();
    final NumberContext tmpAccuracy = StandardType.PERCENT.newPrecision(5);
    TestUtils.assertTrue(tmpModel.validate(Array1D.BIG.copy(expected), tmpAccuracy));
    TestUtils.assertTrue(tmpModel.validate(tmpActualResult, tmpAccuracy));
    final TwiceDifferentiable<Double> tmpObjective = tmpModel.objective().toFunction();
    final double tmpExpObjFuncVal = tmpObjective.invoke(Access1D.asPrimitive1D(expected));
    final double tmpActObjFuncVal = tmpObjective.invoke(Access1D.asPrimitive1D(tmpActualResult));
    TestUtils.assertEquals(tmpExpObjFuncVal, tmpActObjFuncVal, tmpAccuracy);
    TestUtils.assertEquals(expected, tmpActualResult, tmpAccuracy);
    // Test that the LinearSolver can determine feasibility
    final ExpressionsBasedModel relaxedModel = tmpModel.relax(false);
    final Optimisation.Result tmpLinearResult = relaxedModel.minimise();
    TestUtils.assertStateNotLessThanFeasible(tmpLinearResult);
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpModel, tmpAccuracy);
}

Example 5 with ExpressionsBasedModel

use of org.ojalgo.optimisation.ExpressionsBasedModel in project ojAlgo by optimatika.

the class ConvexProblems method builAndTestModel.

static void builAndTestModel(final PrimitiveDenseStore[] matrices, final PrimitiveDenseStore expectedSolution, final NumberContext modelValidationContext, final boolean testSolverDirectly) {
    final MatrixStore<Double> tmpPartQ = expectedSolution.transpose().multiply(matrices[2].multiply(expectedSolution));
    final MatrixStore<Double> tmpPartC = matrices[3].transpose().multiply(expectedSolution);
    final double tmpExpectedValue = tmpPartQ.multiply(HALF.doubleValue()).subtract(tmpPartC).doubleValue(0);
    final Optimisation.Result tmpExpectedResult = new Optimisation.Result(Optimisation.State.OPTIMAL, tmpExpectedValue, expectedSolution);
    final ExpressionsBasedModel tmpModel = ConvexProblems.buildModel(matrices, expectedSolution);
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpModel, modelValidationContext);
    if (DEBUG) {
        tmpModel.options.debug(ConvexSolver.class);
        tmpModel.options.validate = false;
    }
    TestUtils.assertTrue("Expected solution not ok!", tmpModel.validate(tmpExpectedResult, modelValidationContext));
    // The expected solution is written to the variables
    TestUtils.assertTrue("Expected solution not ok!", tmpModel.validate(modelValidationContext));
    // When/if the correct/optimal solution is used to kickStart ojAlgo should return that solution
    final Result tmpInitialisedModelResult = tmpModel.minimise();
    TestUtils.assertStateNotLessThanOptimal(tmpInitialisedModelResult);
    TestUtils.assertEquals(tmpExpectedResult, tmpInitialisedModelResult, modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpInitialisedModelResult.getValue(), modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpModel.objective().evaluate(tmpInitialisedModelResult).doubleValue(), modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpModel.objective().toFunction().invoke(expectedSolution).doubleValue(), modelValidationContext);
    for (final Variable tmpVariable : tmpModel.getVariables()) {
        tmpVariable.setValue(null);
    }
    // Initial variable values have been cleared
    final Result tmpUninitialisedModelResult = tmpModel.minimise();
    TestUtils.assertStateNotLessThanOptimal(tmpUninitialisedModelResult);
    TestUtils.assertEquals(tmpExpectedResult, tmpUninitialisedModelResult, modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpUninitialisedModelResult.getValue(), modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpModel.objective().evaluate(tmpUninitialisedModelResult).doubleValue(), modelValidationContext);
    TestUtils.assertEquals(tmpExpectedValue, tmpModel.objective().toFunction().invoke(expectedSolution).doubleValue(), modelValidationContext);
    if (testSolverDirectly) {
        final ConvexSolver.Builder tmpBuilder = new ConvexSolver.Builder(matrices);
        final ConvexSolver tmpSolver = tmpBuilder.build();
        // tmpSolver.options.debug(ConvexSolver.class);
        // tmpSolver.options.validate = false;
        final Optimisation.Result tmpResult = tmpSolver.solve();
        TestUtils.assertStateNotLessThanOptimal(tmpResult);
        TestUtils.assertEquals(tmpExpectedResult, tmpResult, NumberContext.getGeneral(2, 4));
        TestUtils.assertEquals(tmpExpectedValue, tmpModel.objective().evaluate(tmpResult).doubleValue(), NumberContext.getGeneral(4, 8));
    }
}