Examples of org.apache.commons.math3.optim.SimpleValueChecker

org.apache.commons.math3.optim.SimpleValueChecker
Simple implementation of the {@link ConvergenceChecker} interface usingonly objective function values. Convergence is considered to have been reached if either the relative difference between the objective function values is smaller than a threshold or if either the absolute difference between the objective function values is smaller than another threshold.
The {@link #converged(int,PointValuePair,PointValuePair) converged}method will also return {@code true} if the number of iterations has been set(see {@link #SimpleValueChecker(double,double,int) this constructor}). @since 3.0

    public void testBoundsUnsupported() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        optimizer.optimize(new MaxEval(100),
                           problem.getObjectiveFunction(),
                           problem.getObjectiveFunctionGradient(),
                           GoalType.MINIMIZE,

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    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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            = new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
                                new double[] { 4.0, 6.0, 1.0 });


        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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                    }
                };


        NonLinearConjugateGradientOptimizer optimizer
           = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                     new SimpleValueChecker(1e-13, 1e-13),
                                                     1e-7, 1e-7, 1,
                                                     preconditioner);


        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),

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                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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                {  8.0, 6.0, 10.0,  9.0 },
                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-13, 1e-13),
                                                      1e-15, 1e-15, 1);
        PointValuePair optimum1
            = optimizer.optimize(new MaxEval(200),
                                 problem1.getObjectiveFunction(),
                                 problem1.getObjectiveFunctionGradient(),

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                { 2.0, 0.0,  1.0, 0.0 }
        }, new double[] { 7.0, 3.0, 5.0 });


        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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                 { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
                 { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
        NonLinearConjugateGradientOptimizer optimizer
           = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                     new SimpleValueChecker(1e-6, 1e-6),
                                                     1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),

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