Examples with NumberContext org.ojalgo.type.context.NumberContext used on opensource projects

Example 51 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class ExpressionsBasedModel method validate.

public boolean validate(final Printer appender) {
    final NumberContext context = options.feasibility;
    final Result solution = this.getVariableValues(context);
    return this.validate(solution, context, appender);
}

Example 52 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class JacobiSolver method solve.

@SuppressWarnings("unchecked")
public final MatrixStore<Double> solve(final Access2D<?> body, final Access2D<?> rhs, final PhysicalStore<Double> current) throws RecoverableCondition {
    MatrixStore<Double> tmpBody = null;
    if ((body instanceof MatrixStore<?>) && (body.get(0L) instanceof Double)) {
        tmpBody = (MatrixStore<Double>) body;
    } else {
        tmpBody = MatrixStore.PRIMITIVE.makeWrapper(body).get();
    }
    final MatrixStore<Double> tmpBodyDiagonal = PrimitiveDenseStore.FACTORY.columns(tmpBody.sliceDiagonal(0L, 0L));
    MatrixStore<Double> tmpRHS = null;
    if ((rhs instanceof MatrixStore<?>) && (rhs.get(0L) instanceof Double)) {
        tmpRHS = (MatrixStore<Double>) rhs;
    } else {
        tmpRHS = MatrixStore.PRIMITIVE.makeWrapper(rhs).get();
    }
    final PhysicalStore<Double> tmpIncrement = this.preallocate(body, rhs);
    double tmpNormErr = POSITIVE_INFINITY;
    final double tmpNormRHS = tmpRHS.aggregateAll(Aggregator.NORM2);
    int tmpIterations = 0;
    final int tmpLimit = this.getIterationsLimit();
    final NumberContext tmpCntxt = this.getAccuracyContext();
    final double tmpRelaxation = this.getRelaxationFactor();
    do {
        current.premultiply(tmpBody).operateOnMatching(tmpRHS, SUBTRACT).supplyTo(tmpIncrement);
        tmpNormErr = tmpIncrement.aggregateAll(Aggregator.NORM2);
        tmpIncrement.modifyMatching(DIVIDE, tmpBodyDiagonal);
        if (this.getAccuracyContext().isDifferent(ONE, tmpRelaxation)) {
            tmpIncrement.multiply(tmpRelaxation);
        }
        current.modifyMatching(ADD, tmpIncrement);
        tmpIterations++;
        if (this.isDebugPrinterSet()) {
            this.debug(tmpIterations, current);
        }
    } while ((tmpIterations < tmpLimit) && !tmpCntxt.isSmall(tmpNormRHS, tmpNormErr));
    return current;
}

Example 53 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class ConvexProblems method testP20081014.

/**
 * <p>
 * I'm trying to solve some quadratic programming systems using version 24. The ActiveSetSolver does not
 * always converge to a solution, but throws an exception, "Matrix is singular" (The exception is thrown
 * by org.ojalgo.matrix.jama.LUDecomposition). The thing is that if I run Matlabs quadprog method on the
 * exact same system, a solution is found without problems. Here is the code that produces the exception:
 * </p>
 * <p>
 * 2015-02-21: Extended the test case with a few alternatives using ExpressionsBasedModel. Numerically
 * difficult problem as the formulation includes both large and very small parameters (like 1000000000 and
 * -7.646043242556307E-15).
 * </p>
 */
@Test
public void testP20081014() {
    final PhysicalStore.Factory<Double, PrimitiveDenseStore> tmpFactory = PrimitiveDenseStore.FACTORY;
    final PrimitiveDenseStore[] tmpSystem = new PrimitiveDenseStore[6];
    // {[AE], [BE], [Q], [C], [AI], [BI]}
    tmpSystem[0] = tmpFactory.rows(new double[][] { { -0.0729971273939726, -0.31619624199405116, -0.14365990081105298, -3.4914813388431334E-15, 0.9963066090106673, 0.9989967493404447, 1.0, 0.0, 0.0 }, { -2.5486810808521023E-16, 3.6687950405257466, 3.2047109656515507, 1.0, 0.08586699506600544, 0.04478275122437895, 0.0, 1.0, 0.0 }, // AE
    { -7.646043242556307E-15, -107.21808503782593, -97.434268076846, 30.0, -11.54276933307617, 7.647488207332634, 0.0, 0, 1.0 } });
    // BE
    tmpSystem[1] = tmpFactory.rows(new double[][] { { 10.461669614447484 }, { -0.5328532701990767 }, { 15.782527136201711 } });
    final PrimitiveDenseStore tmpQ = tmpFactory.makeEye(9, 9);
    tmpQ.set(3, 3, 10);
    tmpQ.set(4, 4, 10);
    tmpQ.set(5, 5, 10);
    tmpQ.set(6, 6, 1000000000);
    tmpQ.set(7, 7, 1000000000);
    tmpQ.set(8, 8, 1000000000);
    // Q
    tmpSystem[2] = tmpQ;
    // C
    tmpSystem[3] = tmpFactory.rows(new double[][] { { 0 }, { 0 }, { 0 }, { -1 }, { -1 }, { -1 }, { 0 }, { 0 }, { 0 } });
    final double[][] tmpAI = new double[18][9];
    for (int i = 0; i < 9; i++) {
        tmpAI[i][i] = 1;
        tmpAI[i + 9][i] = -1;
    }
    // AI
    tmpSystem[4] = tmpFactory.rows(tmpAI);
    tmpSystem[5] = tmpFactory.rows(new double[][] { { 0 }, { 0.0175 }, { 0.0175 }, { 5 }, { 5 }, { 5 }, { 100000 }, { 100000 }, { 100000 }, { 0 }, { 0.0175 }, { 0.0175 }, { 5 }, { 5 }, { 5 }, { 100000 }, { 100000 }, // BI
    { 100000 } });
    final PrimitiveDenseStore tmpMatlabSolution = tmpFactory.columns(new double[] { 0.00000000000000, -0.01750000000000, -0.01750000000000, 0.88830035195990, 4.56989525276369, 5.00000000000000, 0.90562154243124, -1.91718419629399, 0.06390614020590 });
    // Compare to MatLab using 3 digits and 6 decimal places
    final NumberContext tmpAccuracy = NumberContext.getGeneral(3, 6);
    ConvexProblems.builAndTestModel(tmpSystem, tmpMatlabSolution, tmpAccuracy, false);
}

Example 54 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class ConvexProblems method testP20080819.

/**
 * Another case of looping in the ActiveSetSolver's constraint (de)activation. Slightly different case (I
 * believe). The main reason/difficulty seemed to be that the algorithm would both add and remove
 * constraints in the iteration. Modified the algorithm to only do one thing with each iteration - either
 * add or remove.
 */
@SuppressWarnings("unchecked")
@Test
public void testP20080819() {
    final Factory<PrimitiveMatrix> tmpMtrxFact = PrimitiveMatrix.FACTORY;
    final NumberContext tmpEvalCntxt = StandardType.DECIMAL_032;
    final BasicMatrix[] tmpMatrices = new PrimitiveMatrix[8];
    tmpMatrices[0] = tmpMtrxFact.rows(new double[][] { { 1.0, 1.0, 1.0, 1.0 } });
    tmpMatrices[1] = tmpMtrxFact.rows(new double[][] { { 1.0 } });
    tmpMatrices[2] = tmpMtrxFact.rows(new double[][] { { 15.889978159746546, 7.506345724913546, 0.8416674706550127, 0.435643236753381 }, { 7.506345724913546, 8.325860065234632, 0.4230651628792374, 0.1670802923999648 }, { 0.8416674706550127, 0.4230651628792374, 1.00134099479915, 0.6558469727234849 }, { 0.435643236753381, 0.1670802923999648, 0.6558469727234849, 0.6420451103682865 } });
    tmpMatrices[3] = tmpMtrxFact.rows(new double[][] { { -0.15804736429388952 }, { -0.11226063792731895 }, { -0.10509261785657838 }, { -0.0848686735786316 } });
    tmpMatrices[4] = tmpMtrxFact.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 }, { -0.15804736429388952, -0.11226063792731895, -0.10509261785657838, -0.0848686735786316 }, { -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 } });
    tmpMatrices[5] = tmpMtrxFact.rows(new double[][] { { 0.9 }, { 0.8 }, { 0.7 }, { 0.6 }, { 0.0 }, { -0.1 }, { -0.2 }, { -0.3 }, { -0.4 } });
    tmpMatrices[6] = tmpMtrxFact.rows(new double[][] { { 0.1 }, { 0.2 }, { 0.3 }, { 0.4 } });
    tmpMatrices[7] = null;
    final MatrixStore<Double>[] retVal = new MatrixStore[tmpMatrices.length];
    for (int i = 0; i < retVal.length; i++) {
        if (tmpMatrices[i] != null) {
            if (i == 3) {
                retVal[i] = PrimitiveDenseStore.FACTORY.copy(tmpMatrices[i].negate());
            } else {
                retVal[i] = PrimitiveDenseStore.FACTORY.copy(tmpMatrices[i]);
            }
        }
    }
    final ConvexSolver.Builder tmpBuilder = new ConvexSolver.Builder(retVal);
    // final ActiveSetSolver tmpSolver = new ActiveSetSolver(tmpMatrices);
    final ConvexSolver tmpSolver = tmpBuilder.build();
    // Test that the matrices were input in the right order
    // JUnitUtils.assertEquals(tmpSolver.getAE(), tmpMatrices[0].toPrimitiveStore(),
    // tmpEvalCntxt);
    // JUnitUtils.assertEquals(tmpSolver.getBE(), tmpMatrices[1].toPrimitiveStore(),
    // tmpEvalCntxt);
    // JUnitUtils.assertEquals(tmpSolver.getQ(), tmpMatrices[2].toPrimitiveStore(),
    // tmpEvalCntxt);
    // JUnitUtils.assertEquals(tmpSolver.getC(), tmpMatrices[3].negate().toPrimitiveStore(),
    // tmpEvalCntxt);
    // JUnitUtils.assertEquals(tmpSolver.getAI(), tmpMatrices[4].toPrimitiveStore(),
    // tmpEvalCntxt);
    // JUnitUtils.assertEquals(tmpSolver.getBI(), tmpMatrices[5].toPrimitiveStore(),
    // tmpEvalCntxt);
    final Optimisation.Result tmpResult = tmpSolver.solve();
    TestUtils.assertEquals(tmpMatrices[6], RationalMatrix.FACTORY.columns(tmpResult), tmpEvalCntxt);
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpBuilder, null);
}

Example 55 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class ConvexProblems method testP20090202.

/**
 * "I just tested ojalgo v.26 and experienced nullpointer-exceptions when I tried to optimize any QP
 * without equality-constraints." This test case is the same as (same numbers)
 * {@linkplain #testP20091102a()} but with the equality constraints removed.
 */
@SuppressWarnings("unchecked")
@Test
public void testP20090202() {
    final MatrixStore<Double>[] tmpMtrxs = new MatrixStore[6];
    tmpMtrxs[0] = null;
    tmpMtrxs[1] = null;
    tmpMtrxs[2] = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 3.400491304172128, 5.429710780966787, 5.910932781021423 }, { 5.429710780966787, 23.181215288234903, 27.883770791602895 }, { 5.910932781021423, 27.883770791602895, 34.37266787775051 } });
    tmpMtrxs[3] = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 0.053 }, { 0.0755 }, { 0.0788 } });
    tmpMtrxs[4] = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 }, { -0.053, -0.0755, -0.0788 }, { -1.0, 0.0, 0.0 }, { 0.0, -1.0, 0.0 }, { 0.0, 0.0, -1.0 } });
    tmpMtrxs[5] = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0 }, { 1.0 }, { 1.0 }, { -0.06 }, { 0.0 }, { 0.0 }, { 0.0 } });
    final ConvexSolver.Builder tmpBuilder = new ConvexSolver.Builder(tmpMtrxs);
    final ConvexSolver tmpSolver = tmpBuilder.build();
    final Optimisation.Result tmpResult = tmpSolver.solve();
    TestUtils.assertEquals(State.OPTIMAL, tmpResult.getState());
    final PhysicalStore<BigDecimal> tmpSolution = BigDenseStore.FACTORY.copy(RationalMatrix.FACTORY.columns(tmpResult));
    tmpSolution.modifyAll(new NumberContext(7, 6).getFunction(BigFunction.getSet()));
    for (final BigDecimal tmpBigDecimal : tmpSolution.asList()) {
        if ((tmpBigDecimal.compareTo(BigMath.ZERO) == -1) || (tmpBigDecimal.compareTo(BigMath.ONE) == 1)) {
            TestUtils.fail("!(0.0 <= " + tmpBigDecimal + " <= 1.0)");
        }
    }
    OptimisationConvexTests.assertDirectAndIterativeEquals(tmpBuilder, null);
}