Package org.apache.commons.math3.optimization

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

36
37
38
39
40
41
42
43
44
45
46
47
48
    public void testMinimize1() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 2e-5);
        Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 6e-12);
        Assert.assertTrue(optimizer.getEvaluations() > 60);
        Assert.assertTrue(optimizer.getEvaluations() < 90);
    }
View Full Code Here
51
52
53
54
55
56
57
58
59
60
61
62
63
    public void testMinimize2() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 5e-6);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 6e-6);
        Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 1e-11);
        Assert.assertTrue(optimizer.getEvaluations() > 60);
        Assert.assertTrue(optimizer.getEvaluations() < 90);
    }
View Full Code Here
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-13, 1e-13),
                                                    new BrentSolver(),
                                                    preconditioner);
                                                   
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
        Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
        Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
        Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
        Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);

    }
View Full Code Here
31
32
33
34
35
36
37
38
39
40
41
42
43
    public void testMinimize1() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
        Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
        Assert.assertTrue(optimizer.getEvaluations() > 120);
        Assert.assertTrue(optimizer.getEvaluations() < 150);
    }
View Full Code Here
46
47
48
49
50
51
52
53
54
55
56
57
58
    public void testMinimize2() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            =  optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
        Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
        Assert.assertTrue(optimizer.getEvaluations() > 120);
        Assert.assertTrue(optimizer.getEvaluations() < 150);
    }
View Full Code Here
214
215
216
217
218
219
220
221
222
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
                optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
        Assert.assertTrue(optimum.getValue() > 0.5);
    }
View Full Code Here
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
        }, new double[] { 32, 23, 33, 31 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-13, 1e-13),
                                                    new BrentSolver(1e-15, 1e-15));
        PointValuePair optimum1 =
            optimizer.optimize(200, problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-4);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        PointValuePair optimum2 =
            optimizer.optimize(200, problem2, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
        Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-1);
        Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-1);
        Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-1);
        Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-1);

    }
View Full Code Here
61
62
63
64
65
66
67
68
69
70
71
72
73
    public void testMaximize1() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 });
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
        Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
        Assert.assertTrue(optimizer.getEvaluations() > 120);
        Assert.assertTrue(optimizer.getEvaluations() < 150);
    }
View Full Code Here
76
77
78
79
80
81
82
83
84
85
86
87
88
    public void testMaximize2() {
        SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
        Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12);
        Assert.assertTrue(optimizer.getEvaluations() > 180);
        Assert.assertTrue(optimizer.getEvaluations() < 220);
    }
View Full Code Here
264
265
266
267
268
269
270
271
272
273
        }, new double[] { 7.0, 3.0, 5.0 });

        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 });
        Assert.assertEquals(0, optimum.getValue(), 1.0e-10);

    }
View Full Code Here
TOP

Related Classes of org.apache.commons.math3.optimization.PointValuePair$DataTransferObject

  • 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
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.