Package org.apache.commons.math3.optimization

Examples of org.apache.commons.math3.optimization.PointValuePair

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    @Test
    public void testConstrainedRosen() {
        double[] startPoint = point(DIM, 0.1);
        double[] insigma = point(DIM, 0.1);
        double[][] boundaries = boundaries(DIM, -1, 2);
        PointValuePair expected =
            new PointValuePair(point(DIM,1.0),0.0);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, 2*LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 100000, expected);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, 2*LAMBDA, false, 0, 1e-13,
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    @Test
    public void testDiagonalRosen() {
        double[] startPoint = point(DIM,0.1);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,1.0),0.0);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 1, 1e-13,
                1e-10, 1e-4, 1000000, expected);
     }
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            };

        final double[] start = { 1 };
        // No bounds.
        PointValuePair result = optimizer.optimize(100000, fitnessFunction, GoalType.MINIMIZE,
                                                   start);
        final double resNoBound = result.getPoint()[0];

        // Optimum is near the lower bound.
        final double[] lower = { -20 };
        final double[] upper = { 5e16 };
        result = optimizer.optimize(100000, fitnessFunction, GoalType.MINIMIZE,
                                    start, lower, upper);
        final double resNearLo = result.getPoint()[0];

        // Optimum is near the upper bound.
        lower[0] = -5e16;
        upper[0] = 20;
        result = optimizer.optimize(100000, fitnessFunction, GoalType.MINIMIZE,
                                    start, lower, upper);
        final double resNearHi = result.getPoint()[0];

        // System.out.println("resNoBound=" + resNoBound +
        //                    " resNearLo=" + resNearLo +
        //                    " resNearHi=" + resNearHi);
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        // test diagonalOnly = 0 - slow but normally fewer feval#
        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
                                                  0, new MersenneTwister(), false, null);
        final double[] lB = boundaries == null ? null : boundaries[0];
        final double[] uB = boundaries == null ? null : boundaries[1];
        PointValuePair result = boundaries == null ?
            optim.optimize(maxEvaluations, func, goal,
                           new InitialGuess(startPoint),
                           new CMAESOptimizer.Sigma(inSigma),
                           new CMAESOptimizer.PopulationSize(lambda)) :
            optim.optimize(maxEvaluations, func, goal,
                           new InitialGuess(startPoint),
                           new SimpleBounds(lB, uB),
                           new CMAESOptimizer.Sigma(inSigma),
                           new CMAESOptimizer.PopulationSize(lambda));
        // System.out.println("sol=" + Arrays.toString(result.getPoint()));
        Assert.assertEquals(expected.getValue(), result.getValue(), fTol);
        for (int i = 0; i < dim; i++) {
            Assert.assertEquals(expected.getPoint()[i], result.getPoint()[i], pointTol);
        }
    }
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                                                           1000.0, new double[] { 100.0, 100.0 });

        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

        final PointValuePair optimum
            = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { 1.5, 2.25 });

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

    }
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                                                           1000.0, new double[] { 100.0, 100.0 });

        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

        final PointValuePair optimum
            = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 });

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

    }
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                                                           1000.0, new double[] { 100.0, 100.0 });

        SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-11, 1.0e-20));
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

        final PointValuePair optimum
            = optimizer.optimize(600, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 });

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

    }
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                                                           1000.0, new double[] { 100.0, 100.0 });

        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

        final PointValuePair optimum
            = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 });

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

    }
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                                                           1000.0, new double[] { 100.0, 100.0 });

        SimplexOptimizer optimizer = new SimplexOptimizer(new SimplePointChecker<PointValuePair>(1.0e-10, 1.0e-20));
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

        final PointValuePair optimum
            = optimizer.optimize(400, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 });

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

    }
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    @Test
    public void testRosen() {
        double[] startPoint = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected = new PointValuePair(point(DIM,1.0),0.0);
        doTest(new Rosen(), startPoint, boundaries,
                GoalType.MINIMIZE,
                1e-13, 1e-6, 2000, expected);
     }
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Related Classes of org.apache.commons.math3.optimization.PointValuePair

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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