Examples of GaussNewtonOptimizer

org.apache.commons.math.optimization.general.GaussNewtonOptimizer
Gauss-Newton least-squares solver.
This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration. Either LU decomposition or QR decomposition can be used to solve the normal equations. LU decomposition is faster but QR decomposition is more robust for difficult problems.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $ @since 2.0
org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer
Gauss-Newton least-squares solver.
Constraints are not supported: the call to {@link #optimize(OptimizationData[]) optimize} will throw{@link MathUnsupportedOperationException} if bounds are passed to it.
This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration. Either LU decomposition or QR decomposition can be used to solve the normal equations. LU decomposition is faster but QR decomposition is more robust for difficult problems.
@since 2.0 @deprecated All classes and interfaces in this package are deprecated.The optimizers that were provided here were moved to the {@link org.apache.commons.math3.fitting.leastsquares} package(cf. MATH-1008).
org.apache.commons.math3.optimization.general.GaussNewtonOptimizer
Gauss-Newton least-squares solver.
This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration. Either LU decomposition or QR decomposition can be used to solve the normal equations. LU decomposition is faster but QR decomposition is more robust for difficult problems.
@deprecated As of 3.1 (to be removed in 4.0). @since 2.0

Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 3;


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);


        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], tol);
        }
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final double tol = 1e-100;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.


        doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol, maxEval);


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);


        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], 1e-15);
        }
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

    }


    @Test
    public void testRedundantUnsolvable() {
        // Gauss-Newton should not be able to solve redundant information
        checkUnsolvableProblem(new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-15, 1e-15)), false);
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

     * @param yDegree Degree of the polynomial fitting functions along the
     * y-dimension.
     */
    public SmoothingPolynomialBicubicSplineInterpolator(int xDegree,
                                                        int yDegree) {
        xFitter = new PolynomialFitter(xDegree, new GaussNewtonOptimizer(false));
        yFitter = new PolynomialFitter(yDegree, new GaussNewtonOptimizer(false));
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

    }


    @Test
    public void testRedundantUnsolvable() {
        // Gauss-Newton should not be able to solve redundant information
        checkUnsolvableProblem(new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-15, 1e-15)), false);
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 3;


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);


        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], tol);
        }
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final double tol = 1e-100;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.


        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol, maxEval);


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);


        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], 1e-15);
        }
    }

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Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

        // version 3.1 of the library. It should be removed when GaussNewtonOptimizer
        // will officialy be declared as implementing MultivariateDifferentiableVectorOptimizer
        MultivariateDifferentiableVectorOptimizer underlyingOptimizer =
                new MultivariateDifferentiableVectorOptimizer() {
            private GaussNewtonOptimizer gn =
                    new GaussNewtonOptimizer(true,
                                             new SimpleVectorValueChecker(1.0e-6, 1.0e-6));


            public PointVectorValuePair optimize(int maxEval,
                                                 MultivariateDifferentiableVectorFunction f,
                                                 double[] target,
                                                 double[] weight,
                                                 double[] startPoint) {
                return gn.optimize(maxEval, f, target, weight, startPoint);
            }


            public int getMaxEvaluations() {
                return gn.getMaxEvaluations();
            }


            public int getEvaluations() {
                return gn.getEvaluations();
            }


            public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
                return gn.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =

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