Package org.apache.commons.math3.optim.nonlinear.vector

Examples of org.apache.commons.math3.optim.nonlinear.vector.Target

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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }

            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }
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        // Perform the fit.
        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(maxEval),
                                 model.getModelFunction(),
                                 model.getModelFunctionJacobian(),
                                 new Target(target),
                                 new Weight(weights),
                                 new InitialGuess(initialGuess));
        // Extract the coefficients.
        return optimum.getPointRef();
    }
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            this.factors = new BlockRealMatrix(factors);
            this.target  = target;
        }

        public Target getTarget() {
            return new Target(target);
        }
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    }

    @Test(expected=MathUnsupportedOperationException.class)
    public void testConstraintsUnsupported() {
        createOptimizer().optimize(new MaxEval(100),
                                   new Target(new double[] { 2 }),
                                   new Weight(new double[] { 1 }),
                                   new InitialGuess(new double[] { 1, 2 }),
                                   new SimpleBounds(new double[] { -10, 0 },
                                                    new double[] { 20, 30 }));
    }
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            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));

        optimizer.optimize(new MaxEval(100),
                           circle.getModelFunction(),
                           circle.getModelFunctionJacobian(),
                           new Target(new double[] { 0, 0, 0, 0, 0 }),
                           new Weight(new double[] { 1, 1, 1, 1, 1 }),
                           new InitialGuess(new double[] { 98.680, 47.345 }));
    }
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        try {
            PointVectorValuePair optimum
                = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
                                     function.getModelFunction(),
                                     function.getModelFunctionJacobian(),
                                     new Target(function.getTarget()),
                                     new Weight(function.getWeight()),
                                     new InitialGuess(function.getStartPoint()));
            Assert.assertFalse(exceptionExpected);
            function.checkTheoreticalMinCost(optimizer.getRMS());
            function.checkTheoreticalMinParams(optimum);
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            = dataset.getLeastSquaresProblem();

        optimizer.optimize(new MaxEval(1),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           new Target(y),
                           new Weight(w),
                           new InitialGuess(a));
        final double expected = dataset.getResidualSumOfSquares();
        final double actual = optimizer.getChiSquare();
        Assert.assertEquals(dataset.getName(), expected, actual,
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            = dataset.getLeastSquaresProblem();

        optimizer.optimize(new MaxEval(1),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           new Target(y),
                           new Weight(w),
                           new InitialGuess(a));

        final double expected = FastMath
            .sqrt(dataset.getResidualSumOfSquares() /
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        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(1),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(y),
                                 new Weight(w),
                                 new InitialGuess(a));

        final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
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    public abstract AbstractLeastSquaresOptimizer createOptimizer();

    @Test
    public void testGetIterations() {
        AbstractLeastSquaresOptimizer optim = createOptimizer();
        optim.optimize(new MaxEval(100), new Target(new double[] { 1 }),
                       new Weight(new double[] { 1 }),
                       new InitialGuess(new double[] { 3 }),
                       new ModelFunction(new MultivariateVectorFunction() {
                               public double[] value(double[] point) {
                                   return new double[] {
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Related Classes of org.apache.commons.math3.optim.nonlinear.vector.Target

  • 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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