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From t.@apache.org
Subject [26/82] [partial] [math] Update for next development iteration: commons-math4
Date Mon, 16 Feb 2015 22:39:56 GMT
http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresBuilder.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresBuilder.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresBuilder.java
deleted file mode 100644
index 7b14b37..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresBuilder.java
+++ /dev/null
@@ -1,226 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math3.analysis.MultivariateVectorFunction;
-import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
-import org.apache.commons.math3.linear.ArrayRealVector;
-import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.linear.RealVector;
-import org.apache.commons.math3.optim.ConvergenceChecker;
-import org.apache.commons.math3.optim.PointVectorValuePair;
-
-/**
- * A mutable builder for {@link LeastSquaresProblem}s.
- *
- * @see LeastSquaresFactory
- * @since 3.3
- */
-public class LeastSquaresBuilder {
-
-    /** max evaluations */
-    private int maxEvaluations;
-    /** max iterations */
-    private int maxIterations;
-    /** convergence checker */
-    private ConvergenceChecker<Evaluation> checker;
-    /** model function */
-    private MultivariateJacobianFunction model;
-    /** observed values */
-    private RealVector target;
-    /** initial guess */
-    private RealVector start;
-    /** weight matrix */
-    private RealMatrix weight;
-    /**
-     * Lazy evaluation.
-     *
-     * @since 3.4
-     */
-    private boolean lazyEvaluation;
-    /** Validator.
-     *
-     * @since 3.4
-     */
-    private ParameterValidator paramValidator;
-
-
-    /**
-     * Construct a {@link LeastSquaresProblem} from the data in this builder.
-     *
-     * @return a new {@link LeastSquaresProblem}.
-     */
-    public LeastSquaresProblem build() {
-        return LeastSquaresFactory.create(model,
-                                          target,
-                                          start,
-                                          weight,
-                                          checker,
-                                          maxEvaluations,
-                                          maxIterations,
-                                          lazyEvaluation,
-                                          paramValidator);
-    }
-
-    /**
-     * Configure the max evaluations.
-     *
-     * @param newMaxEvaluations the maximum number of evaluations permitted.
-     * @return this
-     */
-    public LeastSquaresBuilder maxEvaluations(final int newMaxEvaluations) {
-        this.maxEvaluations = newMaxEvaluations;
-        return this;
-    }
-
-    /**
-     * Configure the max iterations.
-     *
-     * @param newMaxIterations the maximum number of iterations permitted.
-     * @return this
-     */
-    public LeastSquaresBuilder maxIterations(final int newMaxIterations) {
-        this.maxIterations = newMaxIterations;
-        return this;
-    }
-
-    /**
-     * Configure the convergence checker.
-     *
-     * @param newChecker the convergence checker.
-     * @return this
-     */
-    public LeastSquaresBuilder checker(final ConvergenceChecker<Evaluation> newChecker) {
-        this.checker = newChecker;
-        return this;
-    }
-
-    /**
-     * Configure the convergence checker.
-     * <p/>
-     * This function is an overloaded version of {@link #checker(ConvergenceChecker)}.
-     *
-     * @param newChecker the convergence checker.
-     * @return this
-     */
-    public LeastSquaresBuilder checkerPair(final ConvergenceChecker<PointVectorValuePair> newChecker) {
-        return this.checker(LeastSquaresFactory.evaluationChecker(newChecker));
-    }
-
-    /**
-     * Configure the model function.
-     *
-     * @param value the model function value
-     * @param jacobian the Jacobian of {@code value}
-     * @return this
-     */
-    public LeastSquaresBuilder model(final MultivariateVectorFunction value,
-                                     final MultivariateMatrixFunction jacobian) {
-        return model(LeastSquaresFactory.model(value, jacobian));
-    }
-
-    /**
-     * Configure the model function.
-     *
-     * @param newModel the model function value and Jacobian
-     * @return this
-     */
-    public LeastSquaresBuilder model(final MultivariateJacobianFunction newModel) {
-        this.model = newModel;
-        return this;
-    }
-
-    /**
-     * Configure the observed data.
-     *
-     * @param newTarget the observed data.
-     * @return this
-     */
-    public LeastSquaresBuilder target(final RealVector newTarget) {
-        this.target = newTarget;
-        return this;
-    }
-
-    /**
-     * Configure the observed data.
-     *
-     * @param newTarget the observed data.
-     * @return this
-     */
-    public LeastSquaresBuilder target(final double[] newTarget) {
-        return target(new ArrayRealVector(newTarget, false));
-    }
-
-    /**
-     * Configure the initial guess.
-     *
-     * @param newStart the initial guess.
-     * @return this
-     */
-    public LeastSquaresBuilder start(final RealVector newStart) {
-        this.start = newStart;
-        return this;
-    }
-
-    /**
-     * Configure the initial guess.
-     *
-     * @param newStart the initial guess.
-     * @return this
-     */
-    public LeastSquaresBuilder start(final double[] newStart) {
-        return start(new ArrayRealVector(newStart, false));
-    }
-
-    /**
-     * Configure the weight matrix.
-     *
-     * @param newWeight the weight matrix
-     * @return this
-     */
-    public LeastSquaresBuilder weight(final RealMatrix newWeight) {
-        this.weight = newWeight;
-        return this;
-    }
-
-    /**
-     * Configure whether evaluation will be lazy or not.
-     *
-     * @param newValue Whether to perform lazy evaluation.
-     * @return this object.
-     *
-     * @since 3.4
-     */
-    public LeastSquaresBuilder lazyEvaluation(final boolean newValue) {
-        lazyEvaluation = newValue;
-        return this;
-    }
-
-    /**
-     * Configure the validator of the model parameters.
-     *
-     * @param newValidator Parameter validator.
-     * @return this object.
-     *
-     * @since 3.4
-     */
-    public LeastSquaresBuilder parameterValidator(final ParameterValidator newValidator) {
-        paramValidator = newValidator;
-        return this;
-    }
-}

http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresFactory.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresFactory.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresFactory.java
deleted file mode 100644
index d483c26..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresFactory.java
+++ /dev/null
@@ -1,528 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-import org.apache.commons.math3.exception.MathIllegalStateException;
-import org.apache.commons.math3.exception.util.LocalizedFormats;
-import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math3.analysis.MultivariateVectorFunction;
-import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
-import org.apache.commons.math3.linear.Array2DRowRealMatrix;
-import org.apache.commons.math3.linear.ArrayRealVector;
-import org.apache.commons.math3.linear.DiagonalMatrix;
-import org.apache.commons.math3.linear.EigenDecomposition;
-import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.linear.RealVector;
-import org.apache.commons.math3.optim.AbstractOptimizationProblem;
-import org.apache.commons.math3.optim.ConvergenceChecker;
-import org.apache.commons.math3.optim.PointVectorValuePair;
-import org.apache.commons.math3.util.FastMath;
-import org.apache.commons.math3.util.Incrementor;
-import org.apache.commons.math3.util.Pair;
-
-/**
- * A Factory for creating {@link LeastSquaresProblem}s.
- *
- * @since 3.3
- */
-public class LeastSquaresFactory {
-
-    /** Prevent instantiation. */
-    private LeastSquaresFactory() {}
-
-    /**
-     * Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
-     * from the given elements. There will be no weights applied (unit weights).
-     *
-     * @param model          the model function. Produces the computed values.
-     * @param observed       the observed (target) values
-     * @param start          the initial guess.
-     * @param weight         the weight matrix
-     * @param checker        convergence checker
-     * @param maxEvaluations the maximum number of times to evaluate the model
-     * @param maxIterations  the maximum number to times to iterate in the algorithm
-     * @param lazyEvaluation Whether the call to {@link Evaluation#evaluate(RealVector)}
-     * will defer the evaluation until access to the value is requested.
-     * @param paramValidator Model parameters validator.
-     * @return the specified General Least Squares problem.
-     *
-     * @since 3.4
-     */
-    public static LeastSquaresProblem create(final MultivariateJacobianFunction model,
-                                             final RealVector observed,
-                                             final RealVector start,
-                                             final RealMatrix weight,
-                                             final ConvergenceChecker<Evaluation> checker,
-                                             final int maxEvaluations,
-                                             final int maxIterations,
-                                             final boolean lazyEvaluation,
-                                             final ParameterValidator paramValidator) {
-        final LeastSquaresProblem p = new LocalLeastSquaresProblem(model,
-                                                                   observed,
-                                                                   start,
-                                                                   checker,
-                                                                   maxEvaluations,
-                                                                   maxIterations,
-                                                                   lazyEvaluation,
-                                                                   paramValidator);
-        if (weight != null) {
-            return weightMatrix(p, weight);
-        } else {
-            return p;
-        }
-    }
-
-    /**
-     * Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
-     * from the given elements. There will be no weights applied (unit weights).
-     *
-     * @param model          the model function. Produces the computed values.
-     * @param observed       the observed (target) values
-     * @param start          the initial guess.
-     * @param checker        convergence checker
-     * @param maxEvaluations the maximum number of times to evaluate the model
-     * @param maxIterations  the maximum number to times to iterate in the algorithm
-     * @return the specified General Least Squares problem.
-     */
-    public static LeastSquaresProblem create(final MultivariateJacobianFunction model,
-                                             final RealVector observed,
-                                             final RealVector start,
-                                             final ConvergenceChecker<Evaluation> checker,
-                                             final int maxEvaluations,
-                                             final int maxIterations) {
-        return create(model,
-                      observed,
-                      start,
-                      null,
-                      checker,
-                      maxEvaluations,
-                      maxIterations,
-                      false,
-                      null);
-    }
-
-    /**
-     * Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
-     * from the given elements.
-     *
-     * @param model          the model function. Produces the computed values.
-     * @param observed       the observed (target) values
-     * @param start          the initial guess.
-     * @param weight         the weight matrix
-     * @param checker        convergence checker
-     * @param maxEvaluations the maximum number of times to evaluate the model
-     * @param maxIterations  the maximum number to times to iterate in the algorithm
-     * @return the specified General Least Squares problem.
-     */
-    public static LeastSquaresProblem create(final MultivariateJacobianFunction model,
-                                             final RealVector observed,
-                                             final RealVector start,
-                                             final RealMatrix weight,
-                                             final ConvergenceChecker<Evaluation> checker,
-                                             final int maxEvaluations,
-                                             final int maxIterations) {
-        return weightMatrix(create(model,
-                                   observed,
-                                   start,
-                                   checker,
-                                   maxEvaluations,
-                                   maxIterations),
-                            weight);
-    }
-
-    /**
-     * Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
-     * from the given elements.
-     * <p>
-     * This factory method is provided for continuity with previous interfaces. Newer
-     * applications should use {@link #create(MultivariateJacobianFunction, RealVector,
-     * RealVector, ConvergenceChecker, int, int)}, or {@link #create(MultivariateJacobianFunction,
-     * RealVector, RealVector, RealMatrix, ConvergenceChecker, int, int)}.
-     *
-     * @param model          the model function. Produces the computed values.
-     * @param jacobian       the jacobian of the model with respect to the parameters
-     * @param observed       the observed (target) values
-     * @param start          the initial guess.
-     * @param weight         the weight matrix
-     * @param checker        convergence checker
-     * @param maxEvaluations the maximum number of times to evaluate the model
-     * @param maxIterations  the maximum number to times to iterate in the algorithm
-     * @return the specified General Least Squares problem.
-     */
-    public static LeastSquaresProblem create(final MultivariateVectorFunction model,
-                                             final MultivariateMatrixFunction jacobian,
-                                             final double[] observed,
-                                             final double[] start,
-                                             final RealMatrix weight,
-                                             final ConvergenceChecker<Evaluation> checker,
-                                             final int maxEvaluations,
-                                             final int maxIterations) {
-        return create(model(model, jacobian),
-                      new ArrayRealVector(observed, false),
-                      new ArrayRealVector(start, false),
-                      weight,
-                      checker,
-                      maxEvaluations,
-                      maxIterations);
-    }
-
-    /**
-     * Apply a dense weight matrix to the {@link LeastSquaresProblem}.
-     *
-     * @param problem the unweighted problem
-     * @param weights the matrix of weights
-     * @return a new {@link LeastSquaresProblem} with the weights applied. The original
-     *         {@code problem} is not modified.
-     */
-    public static LeastSquaresProblem weightMatrix(final LeastSquaresProblem problem,
-                                                   final RealMatrix weights) {
-        final RealMatrix weightSquareRoot = squareRoot(weights);
-        return new LeastSquaresAdapter(problem) {
-            @Override
-            public Evaluation evaluate(final RealVector point) {
-                return new DenseWeightedEvaluation(super.evaluate(point), weightSquareRoot);
-            }
-        };
-    }
-
-    /**
-     * Apply a diagonal weight matrix to the {@link LeastSquaresProblem}.
-     *
-     * @param problem the unweighted problem
-     * @param weights the diagonal of the weight matrix
-     * @return a new {@link LeastSquaresProblem} with the weights applied. The original
-     *         {@code problem} is not modified.
-     */
-    public static LeastSquaresProblem weightDiagonal(final LeastSquaresProblem problem,
-                                                     final RealVector weights) {
-        // TODO more efficient implementation
-        return weightMatrix(problem, new DiagonalMatrix(weights.toArray()));
-    }
-
-    /**
-     * Count the evaluations of a particular problem. The {@code counter} will be
-     * incremented every time {@link LeastSquaresProblem#evaluate(RealVector)} is called on
-     * the <em>returned</em> problem.
-     *
-     * @param problem the problem to track.
-     * @param counter the counter to increment.
-     * @return a least squares problem that tracks evaluations
-     */
-    public static LeastSquaresProblem countEvaluations(final LeastSquaresProblem problem,
-                                                       final Incrementor counter) {
-        return new LeastSquaresAdapter(problem) {
-
-            public Evaluation evaluate(final RealVector point) {
-                counter.incrementCount();
-                return super.evaluate(point);
-            }
-
-            // Delegate the rest.
-        };
-    }
-
-    /**
-     * View a convergence checker specified for a {@link PointVectorValuePair} as one
-     * specified for an {@link Evaluation}.
-     *
-     * @param checker the convergence checker to adapt.
-     * @return a convergence checker that delegates to {@code checker}.
-     */
-    public static ConvergenceChecker<Evaluation> evaluationChecker(final ConvergenceChecker<PointVectorValuePair> checker) {
-        return new ConvergenceChecker<Evaluation>() {
-            public boolean converged(final int iteration,
-                                     final Evaluation previous,
-                                     final Evaluation current) {
-                return checker.converged(
-                        iteration,
-                        new PointVectorValuePair(
-                                previous.getPoint().toArray(),
-                                previous.getResiduals().toArray(),
-                                false),
-                        new PointVectorValuePair(
-                                current.getPoint().toArray(),
-                                current.getResiduals().toArray(),
-                                false)
-                );
-            }
-        };
-    }
-
-    /**
-     * Computes the square-root of the weight matrix.
-     *
-     * @param m Symmetric, positive-definite (weight) matrix.
-     * @return the square-root of the weight matrix.
-     */
-    private static RealMatrix squareRoot(final RealMatrix m) {
-        if (m instanceof DiagonalMatrix) {
-            final int dim = m.getRowDimension();
-            final RealMatrix sqrtM = new DiagonalMatrix(dim);
-            for (int i = 0; i < dim; i++) {
-                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
-            }
-            return sqrtM;
-        } else {
-            final EigenDecomposition dec = new EigenDecomposition(m);
-            return dec.getSquareRoot();
-        }
-    }
-
-    /**
-     * Combine a {@link MultivariateVectorFunction} with a {@link
-     * MultivariateMatrixFunction} to produce a {@link MultivariateJacobianFunction}.
-     *
-     * @param value    the vector value function
-     * @param jacobian the Jacobian function
-     * @return a function that computes both at the same time
-     */
-    public static MultivariateJacobianFunction model(final MultivariateVectorFunction value,
-                                                     final MultivariateMatrixFunction jacobian) {
-        return new LocalValueAndJacobianFunction(value, jacobian);
-    }
-
-    /**
-     * Combine a {@link MultivariateVectorFunction} with a {@link
-     * MultivariateMatrixFunction} to produce a {@link MultivariateJacobianFunction}.
-     *
-     * @param value    the vector value function
-     * @param jacobian the Jacobian function
-     * @return a function that computes both at the same time
-     */
-    private static class LocalValueAndJacobianFunction
-        implements ValueAndJacobianFunction {
-        /** Model. */
-        private final MultivariateVectorFunction value;
-        /** Model's Jacobian. */
-        private final MultivariateMatrixFunction jacobian;
-
-        /**
-         * @param value Model function.
-         * @param jacobian Model's Jacobian function.
-         */
-        LocalValueAndJacobianFunction(final MultivariateVectorFunction value,
-                                      final MultivariateMatrixFunction jacobian) {
-            this.value = value;
-            this.jacobian = jacobian;
-        }
-
-        /** {@inheritDoc} */
-        public Pair<RealVector, RealMatrix> value(final RealVector point) {
-            //TODO get array from RealVector without copying?
-            final double[] p = point.toArray();
-
-            // Evaluate.
-            return new Pair<RealVector, RealMatrix>(computeValue(p),
-                                                    computeJacobian(p));
-        }
-
-        /** {@inheritDoc} */
-        public RealVector computeValue(final double[] params) {
-            return new ArrayRealVector(value.value(params), false);
-        }
-
-        /** {@inheritDoc} */
-        public RealMatrix computeJacobian(final double[] params) {
-            return new Array2DRowRealMatrix(jacobian.value(params), false);
-        }
-    }
-
-
-    /**
-     * A private, "field" immutable (not "real" immutable) implementation of {@link
-     * LeastSquaresProblem}.
-     * @since 3.3
-     */
-    private static class LocalLeastSquaresProblem
-            extends AbstractOptimizationProblem<Evaluation>
-            implements LeastSquaresProblem {
-
-        /** Target values for the model function at optimum. */
-        private final RealVector target;
-        /** Model function. */
-        private final MultivariateJacobianFunction model;
-        /** Initial guess. */
-        private final RealVector start;
-        /** Whether to use lazy evaluation. */
-        private final boolean lazyEvaluation;
-        /** Model parameters validator. */
-        private final ParameterValidator paramValidator;
-
-        /**
-         * Create a {@link LeastSquaresProblem} from the given data.
-         *
-         * @param model          the model function
-         * @param target         the observed data
-         * @param start          the initial guess
-         * @param checker        the convergence checker
-         * @param maxEvaluations the allowed evaluations
-         * @param maxIterations  the allowed iterations
-         * @param lazyEvaluation Whether the call to {@link Evaluation#evaluate(RealVector)}
-         * will defer the evaluation until access to the value is requested.
-         * @param paramValidator Model parameters validator.
-         */
-        LocalLeastSquaresProblem(final MultivariateJacobianFunction model,
-                                 final RealVector target,
-                                 final RealVector start,
-                                 final ConvergenceChecker<Evaluation> checker,
-                                 final int maxEvaluations,
-                                 final int maxIterations,
-                                 final boolean lazyEvaluation,
-                                 final ParameterValidator paramValidator) {
-            super(maxEvaluations, maxIterations, checker);
-            this.target = target;
-            this.model = model;
-            this.start = start;
-            this.lazyEvaluation = lazyEvaluation;
-            this.paramValidator = paramValidator;
-
-            if (lazyEvaluation &&
-                !(model instanceof ValueAndJacobianFunction)) {
-                // Lazy evaluation requires that value and Jacobian
-                // can be computed separately.
-                throw new MathIllegalStateException(LocalizedFormats.INVALID_IMPLEMENTATION,
-                                                    model.getClass().getName());
-            }
-        }
-
-        /** {@inheritDoc} */
-        public int getObservationSize() {
-            return target.getDimension();
-        }
-
-        /** {@inheritDoc} */
-        public int getParameterSize() {
-            return start.getDimension();
-        }
-
-        /** {@inheritDoc} */
-        public RealVector getStart() {
-            return start == null ? null : start.copy();
-        }
-
-        /** {@inheritDoc} */
-        public Evaluation evaluate(final RealVector point) {
-            // Copy so optimizer can change point without changing our instance.
-            final RealVector p = paramValidator == null ?
-                point.copy() :
-                paramValidator.validate(point.copy());
-
-            if (lazyEvaluation) {
-                return new LazyUnweightedEvaluation((ValueAndJacobianFunction) model,
-                                                    target,
-                                                    p);
-            } else {
-                // Evaluate value and jacobian in one function call.
-                final Pair<RealVector, RealMatrix> value = model.value(p);
-                return new UnweightedEvaluation(value.getFirst(),
-                                                value.getSecond(),
-                                                target,
-                                                p);
-            }
-        }
-
-        /**
-         * Container with the model evaluation at a particular point.
-         */
-        private static class UnweightedEvaluation extends AbstractEvaluation {
-            /** Point of evaluation. */
-            private final RealVector point;
-            /** Derivative at point. */
-            private final RealMatrix jacobian;
-            /** Computed residuals. */
-            private final RealVector residuals;
-
-            /**
-             * Create an {@link Evaluation} with no weights.
-             *
-             * @param values   the computed function values
-             * @param jacobian the computed function Jacobian
-             * @param target   the observed values
-             * @param point    the abscissa
-             */
-            private UnweightedEvaluation(final RealVector values,
-                                         final RealMatrix jacobian,
-                                         final RealVector target,
-                                         final RealVector point) {
-                super(target.getDimension());
-                this.jacobian = jacobian;
-                this.point = point;
-                this.residuals = target.subtract(values);
-            }
-
-            /** {@inheritDoc} */
-            public RealMatrix getJacobian() {
-                return jacobian;
-            }
-
-            /** {@inheritDoc} */
-            public RealVector getPoint() {
-                return point;
-            }
-
-            /** {@inheritDoc} */
-            public RealVector getResiduals() {
-                return residuals;
-            }
-        }
-
-        /**
-         * Container with the model <em>lazy</em> evaluation at a particular point.
-         */
-        private static class LazyUnweightedEvaluation extends AbstractEvaluation {
-            /** Point of evaluation. */
-            private final RealVector point;
-            /** Model and Jacobian functions. */
-            private final ValueAndJacobianFunction model;
-            /** Target values for the model function at optimum. */
-            private final RealVector target;
-
-            /**
-             * Create an {@link Evaluation} with no weights.
-             *
-             * @param model  the model function
-             * @param target the observed values
-             * @param point  the abscissa
-             */
-            private LazyUnweightedEvaluation(final ValueAndJacobianFunction model,
-                                             final RealVector target,
-                                             final RealVector point) {
-                super(target.getDimension());
-                // Safe to cast as long as we control usage of this class.
-                this.model = model;
-                this.point = point;
-                this.target = target;
-            }
-
-            /** {@inheritDoc} */
-            public RealMatrix getJacobian() {
-                return model.computeJacobian(point.toArray());
-            }
-
-            /** {@inheritDoc} */
-            public RealVector getPoint() {
-                return point;
-            }
-
-            /** {@inheritDoc} */
-            public RealVector getResiduals() {
-                return target.subtract(model.computeValue(point.toArray()));
-            }
-        }
-    }
-}
-

http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresOptimizer.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresOptimizer.java
deleted file mode 100644
index 50d5b8a..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresOptimizer.java
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-/**
- * An algorithm that can be applied to a non-linear least squares problem.
- *
- * @since 3.3
- */
-public interface LeastSquaresOptimizer {
-
-    /**
-     * Solve the non-linear least squares problem.
-     *
-     *
-     * @param leastSquaresProblem the problem definition, including model function and
-     *                            convergence criteria.
-     * @return The optimum.
-     */
-    Optimum optimize(LeastSquaresProblem leastSquaresProblem);
-
-    /**
-     * The optimum found by the optimizer. This object contains the point, its value, and
-     * some metadata.
-     */
-    //TODO Solution?
-    interface Optimum extends LeastSquaresProblem.Evaluation {
-
-        /**
-         * Get the number of times the model was evaluated in order to produce this
-         * optimum.
-         *
-         * @return the number of model (objective) function evaluations
-         */
-        int getEvaluations();
-
-        /**
-         * Get the number of times the algorithm iterated in order to produce this
-         * optimum. In general least squares it is common to have one {@link
-         * #getEvaluations() evaluation} per iterations.
-         *
-         * @return the number of iterations
-         */
-        int getIterations();
-
-    }
-
-}

http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresProblem.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresProblem.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresProblem.java
deleted file mode 100644
index 097ff81..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresProblem.java
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.linear.RealVector;
-import org.apache.commons.math3.optim.OptimizationProblem;
-
-/**
- * The data necessary to define a non-linear least squares problem.
- * <p>
- * Includes the observed values, computed model function, and
- * convergence/divergence criteria. Weights are implicit in {@link
- * Evaluation#getResiduals()} and {@link Evaluation#getJacobian()}.
- * </p>
- * <p>
- * Instances are typically either created progressively using a {@link
- * LeastSquaresBuilder builder} or created at once using a {@link LeastSquaresFactory
- * factory}.
- * </p>
- * @see LeastSquaresBuilder
- * @see LeastSquaresFactory
- * @see LeastSquaresAdapter
- *
- * @since 3.3
- */
-public interface LeastSquaresProblem extends OptimizationProblem<LeastSquaresProblem.Evaluation> {
-
-    /**
-     * Gets the initial guess.
-     *
-     * @return the initial guess values.
-     */
-    RealVector getStart();
-
-    /**
-     * Get the number of observations (rows in the Jacobian) in this problem.
-     *
-     * @return the number of scalar observations
-     */
-    int getObservationSize();
-
-    /**
-     * Get the number of parameters (columns in the Jacobian) in this problem.
-     *
-     * @return the number of scalar parameters
-     */
-    int getParameterSize();
-
-    /**
-     * Evaluate the model at the specified point.
-     *
-     *
-     * @param point the parameter values.
-     * @return the model's value and derivative at the given point.
-     * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
-     *          if the maximal number of evaluations (of the model vector function) is
-     *          exceeded.
-     */
-    Evaluation evaluate(RealVector point);
-
-    /**
-     * An evaluation of a {@link LeastSquaresProblem} at a particular point. This class
-     * also computes several quantities derived from the value and its Jacobian.
-     */
-    public interface Evaluation {
-
-        /**
-         * Get the covariance matrix of the optimized parameters. <br/> Note that this
-         * operation involves the inversion of the <code>J<sup>T</sup>J</code> matrix,
-         * where {@code J} is the Jacobian matrix. The {@code threshold} parameter is a
-         * way for the caller to specify that the result of this computation should be
-         * considered meaningless, and thus trigger an exception.
-         *
-         *
-         * @param threshold Singularity threshold.
-         * @return the covariance matrix.
-         * @throws org.apache.commons.math3.linear.SingularMatrixException
-         *          if the covariance matrix cannot be computed (singular problem).
-         */
-        RealMatrix getCovariances(double threshold);
-
-        /**
-         * Get an estimate of the standard deviation of the parameters. The returned
-         * values are the square root of the diagonal coefficients of the covariance
-         * matrix, {@code sd(a[i]) ~= sqrt(C[i][i])}, where {@code a[i]} is the optimized
-         * value of the {@code i}-th parameter, and {@code C} is the covariance matrix.
-         *
-         *
-         * @param covarianceSingularityThreshold Singularity threshold (see {@link
-         *                                       #getCovariances(double) computeCovariances}).
-         * @return an estimate of the standard deviation of the optimized parameters
-         * @throws org.apache.commons.math3.linear.SingularMatrixException
-         *          if the covariance matrix cannot be computed.
-         */
-        RealVector getSigma(double covarianceSingularityThreshold);
-
-        /**
-         * Get the normalized cost. It is the square-root of the sum of squared of
-         * the residuals, divided by the number of measurements.
-         *
-         * @return the cost.
-         */
-        double getRMS();
-
-        /**
-         * Get the weighted Jacobian matrix.
-         *
-         * @return the weighted Jacobian: W<sup>1/2</sup> J.
-         * @throws org.apache.commons.math3.exception.DimensionMismatchException
-         * if the Jacobian dimension does not match problem dimension.
-         */
-        RealMatrix getJacobian();
-
-        /**
-         * Get the cost.
-         *
-         * @return the cost.
-         * @see #getResiduals()
-         */
-        double getCost();
-
-        /**
-         * Get the weighted residuals. The residual is the difference between the
-         * observed (target) values and the model (objective function) value. There is one
-         * residual for each element of the vector-valued function. The raw residuals are
-         * then multiplied by the square root of the weight matrix.
-         *
-         * @return the weighted residuals: W<sup>1/2</sup> K.
-         * @throws org.apache.commons.math3.exception.DimensionMismatchException
-         * if the residuals have the wrong length.
-         */
-        RealVector getResiduals();
-
-        /**
-         * Get the abscissa (independent variables) of this evaluation.
-         *
-         * @return the point provided to {@link #evaluate(RealVector)}.
-         */
-        RealVector getPoint();
-    }
-}

http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizer.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizer.java
deleted file mode 100644
index 358d240..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/LevenbergMarquardtOptimizer.java
+++ /dev/null
@@ -1,1042 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-import java.util.Arrays;
-
-import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
-import org.apache.commons.math3.linear.ArrayRealVector;
-import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.exception.ConvergenceException;
-import org.apache.commons.math3.exception.util.LocalizedFormats;
-import org.apache.commons.math3.optim.ConvergenceChecker;
-import org.apache.commons.math3.util.Incrementor;
-import org.apache.commons.math3.util.Precision;
-import org.apache.commons.math3.util.FastMath;
-
-
-/**
- * This class solves a least-squares problem using the Levenberg-Marquardt
- * algorithm.
- *
- * <p>This implementation <em>should</em> work even for over-determined systems
- * (i.e. systems having more point than equations). Over-determined systems
- * are solved by ignoring the point which have the smallest impact according
- * to their jacobian column norm. Only the rank of the matrix and some loop bounds
- * are changed to implement this.</p>
- *
- * <p>The resolution engine is a simple translation of the MINPACK <a
- * href="http://www.netlib.org/minpack/lmder.f">lmder</a> routine with minor
- * changes. The changes include the over-determined resolution, the use of
- * inherited convergence checker and the Q.R. decomposition which has been
- * rewritten following the algorithm described in the
- * P. Lascaux and R. Theodor book <i>Analyse num&eacute;rique matricielle
- * appliqu&eacute;e &agrave; l'art de l'ing&eacute;nieur</i>, Masson 1986.</p>
- * <p>The authors of the original fortran version are:
- * <ul>
- * <li>Argonne National Laboratory. MINPACK project. March 1980</li>
- * <li>Burton S. Garbow</li>
- * <li>Kenneth E. Hillstrom</li>
- * <li>Jorge J. More</li>
- * </ul>
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for convenience, it
- * is reproduced below.</p>
- *
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- *    Minpack Copyright Notice (1999) University of Chicago.
- *    All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- *  <li>Redistributions of source code must retain the above copyright
- *      notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- *     copyright notice, this list of conditions and the following
- *     disclaimer in the documentation and/or other materials provided
- *     with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- *     must include the following acknowledgment:
- *     <code>This product includes software developed by the University of
- *           Chicago, as Operator of Argonne National Laboratory.</code>
- *     Alternately, this acknowledgment may appear in the software itself,
- *     if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- *     WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- *     UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- *     THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- *     IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- *     OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- *     OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- *     OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- *     USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- *     THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- *     DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- *     UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- *     BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- *     HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- *     ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- *     INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- *     ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- *     PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- *     SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- *     (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- *     EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- *     POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
- *
- * @since 3.3
- */
-public class LevenbergMarquardtOptimizer implements LeastSquaresOptimizer {
-
-    /** Twice the "epsilon machine". */
-    private static final double TWO_EPS = 2 * Precision.EPSILON;
-
-    /* configuration parameters */
-    /** Positive input variable used in determining the initial step bound. */
-    private final double initialStepBoundFactor;
-    /** Desired relative error in the sum of squares. */
-    private final double costRelativeTolerance;
-    /**  Desired relative error in the approximate solution parameters. */
-    private final double parRelativeTolerance;
-    /** Desired max cosine on the orthogonality between the function vector
-     * and the columns of the jacobian. */
-    private final double orthoTolerance;
-    /** Threshold for QR ranking. */
-    private final double qrRankingThreshold;
-
-    /** Default constructor.
-     * <p>
-     * The default values for the algorithm settings are:
-     * <ul>
-     *  <li>Initial step bound factor: 100</li>
-     *  <li>Cost relative tolerance: 1e-10</li>
-     *  <li>Parameters relative tolerance: 1e-10</li>
-     *  <li>Orthogonality tolerance: 1e-10</li>
-     *  <li>QR ranking threshold: {@link Precision#SAFE_MIN}</li>
-     * </ul>
-     **/
-    public LevenbergMarquardtOptimizer() {
-        this(100, 1e-10, 1e-10, 1e-10, Precision.SAFE_MIN);
-    }
-
-    /**
-     * Construct an instance with all parameters specified.
-     *
-     * @param initialStepBoundFactor initial step bound factor
-     * @param costRelativeTolerance  cost relative tolerance
-     * @param parRelativeTolerance   parameters relative tolerance
-     * @param orthoTolerance         orthogonality tolerance
-     * @param qrRankingThreshold     threshold in the QR decomposition. Columns with a 2
-     *                               norm less than this threshold are considered to be
-     *                               all 0s.
-     */
-    public LevenbergMarquardtOptimizer(
-            final double initialStepBoundFactor,
-            final double costRelativeTolerance,
-            final double parRelativeTolerance,
-            final double orthoTolerance,
-            final double qrRankingThreshold) {
-        this.initialStepBoundFactor = initialStepBoundFactor;
-        this.costRelativeTolerance = costRelativeTolerance;
-        this.parRelativeTolerance = parRelativeTolerance;
-        this.orthoTolerance = orthoTolerance;
-        this.qrRankingThreshold = qrRankingThreshold;
-    }
-
-    /**
-     * @param newInitialStepBoundFactor Positive input variable used in
-     * determining the initial step bound. This bound is set to the
-     * product of initialStepBoundFactor and the euclidean norm of
-     * {@code diag * x} if non-zero, or else to {@code newInitialStepBoundFactor}
-     * itself. In most cases factor should lie in the interval
-     * {@code (0.1, 100.0)}. {@code 100} is a generally recommended value.
-     * of the matrix is reduced.
-     * @return a new instance.
-     */
-    public LevenbergMarquardtOptimizer withInitialStepBoundFactor(double newInitialStepBoundFactor) {
-        return new LevenbergMarquardtOptimizer(
-                newInitialStepBoundFactor,
-                costRelativeTolerance,
-                parRelativeTolerance,
-                orthoTolerance,
-                qrRankingThreshold);
-    }
-
-    /**
-     * @param newCostRelativeTolerance Desired relative error in the sum of squares.
-     * @return a new instance.
-     */
-    public LevenbergMarquardtOptimizer withCostRelativeTolerance(double newCostRelativeTolerance) {
-        return new LevenbergMarquardtOptimizer(
-                initialStepBoundFactor,
-                newCostRelativeTolerance,
-                parRelativeTolerance,
-                orthoTolerance,
-                qrRankingThreshold);
-    }
-
-    /**
-     * @param newParRelativeTolerance Desired relative error in the approximate solution
-     * parameters.
-     * @return a new instance.
-     */
-    public LevenbergMarquardtOptimizer withParameterRelativeTolerance(double newParRelativeTolerance) {
-        return new LevenbergMarquardtOptimizer(
-                initialStepBoundFactor,
-                costRelativeTolerance,
-                newParRelativeTolerance,
-                orthoTolerance,
-                qrRankingThreshold);
-    }
-
-    /**
-     * Modifies the given parameter.
-     *
-     * @param newOrthoTolerance Desired max cosine on the orthogonality between
-     * the function vector and the columns of the Jacobian.
-     * @return a new instance.
-     */
-    public LevenbergMarquardtOptimizer withOrthoTolerance(double newOrthoTolerance) {
-        return new LevenbergMarquardtOptimizer(
-                initialStepBoundFactor,
-                costRelativeTolerance,
-                parRelativeTolerance,
-                newOrthoTolerance,
-                qrRankingThreshold);
-    }
-
-    /**
-     * @param newQRRankingThreshold Desired threshold for QR ranking.
-     * If the squared norm of a column vector is smaller or equal to this
-     * threshold during QR decomposition, it is considered to be a zero vector
-     * and hence the rank of the matrix is reduced.
-     * @return a new instance.
-     */
-    public LevenbergMarquardtOptimizer withRankingThreshold(double newQRRankingThreshold) {
-        return new LevenbergMarquardtOptimizer(
-                initialStepBoundFactor,
-                costRelativeTolerance,
-                parRelativeTolerance,
-                orthoTolerance,
-                newQRRankingThreshold);
-    }
-
-    /**
-     * Gets the value of a tuning parameter.
-     * @see #withInitialStepBoundFactor(double)
-     *
-     * @return the parameter's value.
-     */
-    public double getInitialStepBoundFactor() {
-        return initialStepBoundFactor;
-    }
-
-    /**
-     * Gets the value of a tuning parameter.
-     * @see #withCostRelativeTolerance(double)
-     *
-     * @return the parameter's value.
-     */
-    public double getCostRelativeTolerance() {
-        return costRelativeTolerance;
-    }
-
-    /**
-     * Gets the value of a tuning parameter.
-     * @see #withParameterRelativeTolerance(double)
-     *
-     * @return the parameter's value.
-     */
-    public double getParameterRelativeTolerance() {
-        return parRelativeTolerance;
-    }
-
-    /**
-     * Gets the value of a tuning parameter.
-     * @see #withOrthoTolerance(double)
-     *
-     * @return the parameter's value.
-     */
-    public double getOrthoTolerance() {
-        return orthoTolerance;
-    }
-
-    /**
-     * Gets the value of a tuning parameter.
-     * @see #withRankingThreshold(double)
-     *
-     * @return the parameter's value.
-     */
-    public double getRankingThreshold() {
-        return qrRankingThreshold;
-    }
-
-    /** {@inheritDoc} */
-    public Optimum optimize(final LeastSquaresProblem problem) {
-        // Pull in relevant data from the problem as locals.
-        final int nR = problem.getObservationSize(); // Number of observed data.
-        final int nC = problem.getParameterSize(); // Number of parameters.
-        // Counters.
-        final Incrementor iterationCounter = problem.getIterationCounter();
-        final Incrementor evaluationCounter = problem.getEvaluationCounter();
-        // Convergence criterion.
-        final ConvergenceChecker<Evaluation> checker = problem.getConvergenceChecker();
-
-        // arrays shared with the other private methods
-        final int solvedCols  = FastMath.min(nR, nC);
-        /* Parameters evolution direction associated with lmPar. */
-        double[] lmDir = new double[nC];
-        /* Levenberg-Marquardt parameter. */
-        double lmPar = 0;
-
-        // local point
-        double   delta   = 0;
-        double   xNorm   = 0;
-        double[] diag    = new double[nC];
-        double[] oldX    = new double[nC];
-        double[] oldRes  = new double[nR];
-        double[] qtf     = new double[nR];
-        double[] work1   = new double[nC];
-        double[] work2   = new double[nC];
-        double[] work3   = new double[nC];
-
-
-        // Evaluate the function at the starting point and calculate its norm.
-        evaluationCounter.incrementCount();
-        //value will be reassigned in the loop
-        Evaluation current = problem.evaluate(problem.getStart());
-        double[] currentResiduals = current.getResiduals().toArray();
-        double currentCost = current.getCost();
-        double[] currentPoint = current.getPoint().toArray();
-
-        // Outer loop.
-        boolean firstIteration = true;
-        while (true) {
-            iterationCounter.incrementCount();
-
-            final Evaluation previous = current;
-
-            // QR decomposition of the jacobian matrix
-            final InternalData internalData
-                    = qrDecomposition(current.getJacobian(), solvedCols);
-            final double[][] weightedJacobian = internalData.weightedJacobian;
-            final int[] permutation = internalData.permutation;
-            final double[] diagR = internalData.diagR;
-            final double[] jacNorm = internalData.jacNorm;
-
-            //residuals already have weights applied
-            double[] weightedResidual = currentResiduals;
-            for (int i = 0; i < nR; i++) {
-                qtf[i] = weightedResidual[i];
-            }
-
-            // compute Qt.res
-            qTy(qtf, internalData);
-
-            // now we don't need Q anymore,
-            // so let jacobian contain the R matrix with its diagonal elements
-            for (int k = 0; k < solvedCols; ++k) {
-                int pk = permutation[k];
-                weightedJacobian[k][pk] = diagR[pk];
-            }
-
-            if (firstIteration) {
-                // scale the point according to the norms of the columns
-                // of the initial jacobian
-                xNorm = 0;
-                for (int k = 0; k < nC; ++k) {
-                    double dk = jacNorm[k];
-                    if (dk == 0) {
-                        dk = 1.0;
-                    }
-                    double xk = dk * currentPoint[k];
-                    xNorm  += xk * xk;
-                    diag[k] = dk;
-                }
-                xNorm = FastMath.sqrt(xNorm);
-
-                // initialize the step bound delta
-                delta = (xNorm == 0) ? initialStepBoundFactor : (initialStepBoundFactor * xNorm);
-            }
-
-            // check orthogonality between function vector and jacobian columns
-            double maxCosine = 0;
-            if (currentCost != 0) {
-                for (int j = 0; j < solvedCols; ++j) {
-                    int    pj = permutation[j];
-                    double s  = jacNorm[pj];
-                    if (s != 0) {
-                        double sum = 0;
-                        for (int i = 0; i <= j; ++i) {
-                            sum += weightedJacobian[i][pj] * qtf[i];
-                        }
-                        maxCosine = FastMath.max(maxCosine, FastMath.abs(sum) / (s * currentCost));
-                    }
-                }
-            }
-            if (maxCosine <= orthoTolerance) {
-                // Convergence has been reached.
-                return new OptimumImpl(
-                        current,
-                        evaluationCounter.getCount(),
-                        iterationCounter.getCount());
-            }
-
-            // rescale if necessary
-            for (int j = 0; j < nC; ++j) {
-                diag[j] = FastMath.max(diag[j], jacNorm[j]);
-            }
-
-            // Inner loop.
-            for (double ratio = 0; ratio < 1.0e-4;) {
-
-                // save the state
-                for (int j = 0; j < solvedCols; ++j) {
-                    int pj = permutation[j];
-                    oldX[pj] = currentPoint[pj];
-                }
-                final double previousCost = currentCost;
-                double[] tmpVec = weightedResidual;
-                weightedResidual = oldRes;
-                oldRes    = tmpVec;
-
-                // determine the Levenberg-Marquardt parameter
-                lmPar = determineLMParameter(qtf, delta, diag,
-                                     internalData, solvedCols,
-                                     work1, work2, work3, lmDir, lmPar);
-
-                // compute the new point and the norm of the evolution direction
-                double lmNorm = 0;
-                for (int j = 0; j < solvedCols; ++j) {
-                    int pj = permutation[j];
-                    lmDir[pj] = -lmDir[pj];
-                    currentPoint[pj] = oldX[pj] + lmDir[pj];
-                    double s = diag[pj] * lmDir[pj];
-                    lmNorm  += s * s;
-                }
-                lmNorm = FastMath.sqrt(lmNorm);
-                // on the first iteration, adjust the initial step bound.
-                if (firstIteration) {
-                    delta = FastMath.min(delta, lmNorm);
-                }
-
-                // Evaluate the function at x + p and calculate its norm.
-                evaluationCounter.incrementCount();
-                current = problem.evaluate(new ArrayRealVector(currentPoint));
-                currentResiduals = current.getResiduals().toArray();
-                currentCost = current.getCost();
-                currentPoint = current.getPoint().toArray();
-
-                // compute the scaled actual reduction
-                double actRed = -1.0;
-                if (0.1 * currentCost < previousCost) {
-                    double r = currentCost / previousCost;
-                    actRed = 1.0 - r * r;
-                }
-
-                // compute the scaled predicted reduction
-                // and the scaled directional derivative
-                for (int j = 0; j < solvedCols; ++j) {
-                    int pj = permutation[j];
-                    double dirJ = lmDir[pj];
-                    work1[j] = 0;
-                    for (int i = 0; i <= j; ++i) {
-                        work1[i] += weightedJacobian[i][pj] * dirJ;
-                    }
-                }
-                double coeff1 = 0;
-                for (int j = 0; j < solvedCols; ++j) {
-                    coeff1 += work1[j] * work1[j];
-                }
-                double pc2 = previousCost * previousCost;
-                coeff1 /= pc2;
-                double coeff2 = lmPar * lmNorm * lmNorm / pc2;
-                double preRed = coeff1 + 2 * coeff2;
-                double dirDer = -(coeff1 + coeff2);
-
-                // ratio of the actual to the predicted reduction
-                ratio = (preRed == 0) ? 0 : (actRed / preRed);
-
-                // update the step bound
-                if (ratio <= 0.25) {
-                    double tmp =
-                        (actRed < 0) ? (0.5 * dirDer / (dirDer + 0.5 * actRed)) : 0.5;
-                        if ((0.1 * currentCost >= previousCost) || (tmp < 0.1)) {
-                            tmp = 0.1;
-                        }
-                        delta = tmp * FastMath.min(delta, 10.0 * lmNorm);
-                        lmPar /= tmp;
-                } else if ((lmPar == 0) || (ratio >= 0.75)) {
-                    delta = 2 * lmNorm;
-                    lmPar *= 0.5;
-                }
-
-                // test for successful iteration.
-                if (ratio >= 1.0e-4) {
-                    // successful iteration, update the norm
-                    firstIteration = false;
-                    xNorm = 0;
-                    for (int k = 0; k < nC; ++k) {
-                        double xK = diag[k] * currentPoint[k];
-                        xNorm += xK * xK;
-                    }
-                    xNorm = FastMath.sqrt(xNorm);
-
-                    // tests for convergence.
-                    if (checker != null && checker.converged(iterationCounter.getCount(), previous, current)) {
-                        return new OptimumImpl(current, evaluationCounter.getCount(), iterationCounter.getCount());
-                    }
-                } else {
-                    // failed iteration, reset the previous values
-                    currentCost = previousCost;
-                    for (int j = 0; j < solvedCols; ++j) {
-                        int pj = permutation[j];
-                        currentPoint[pj] = oldX[pj];
-                    }
-                    tmpVec    = weightedResidual;
-                    weightedResidual = oldRes;
-                    oldRes    = tmpVec;
-                    // Reset "current" to previous values.
-                    current = previous;
-                }
-
-                // Default convergence criteria.
-                if ((FastMath.abs(actRed) <= costRelativeTolerance &&
-                     preRed <= costRelativeTolerance &&
-                     ratio <= 2.0) ||
-                    delta <= parRelativeTolerance * xNorm) {
-                    return new OptimumImpl(current, evaluationCounter.getCount(), iterationCounter.getCount());
-                }
-
-                // tests for termination and stringent tolerances
-                if (FastMath.abs(actRed) <= TWO_EPS &&
-                    preRed <= TWO_EPS &&
-                    ratio <= 2.0) {
-                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_COST_RELATIVE_TOLERANCE,
-                                                   costRelativeTolerance);
-                } else if (delta <= TWO_EPS * xNorm) {
-                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE,
-                                                   parRelativeTolerance);
-                } else if (maxCosine <= TWO_EPS) {
-                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_ORTHOGONALITY_TOLERANCE,
-                                                   orthoTolerance);
-                }
-            }
-        }
-    }
-
-    /**
-     * Holds internal data.
-     * This structure was created so that all optimizer fields can be "final".
-     * Code should be further refactored in order to not pass around arguments
-     * that will modified in-place (cf. "work" arrays).
-     */
-    private static class InternalData {
-        /** Weighted Jacobian. */
-        private final double[][] weightedJacobian;
-        /** Columns permutation array. */
-        private final int[] permutation;
-        /** Rank of the Jacobian matrix. */
-        private final int rank;
-        /** Diagonal elements of the R matrix in the QR decomposition. */
-        private final double[] diagR;
-        /** Norms of the columns of the jacobian matrix. */
-        private final double[] jacNorm;
-        /** Coefficients of the Householder transforms vectors. */
-        private final double[] beta;
-
-        /**
-         * @param weightedJacobian Weighted Jacobian.
-         * @param permutation Columns permutation array.
-         * @param rank Rank of the Jacobian matrix.
-         * @param diagR Diagonal elements of the R matrix in the QR decomposition.
-         * @param jacNorm Norms of the columns of the jacobian matrix.
-         * @param beta Coefficients of the Householder transforms vectors.
-         */
-        InternalData(double[][] weightedJacobian,
-                     int[] permutation,
-                     int rank,
-                     double[] diagR,
-                     double[] jacNorm,
-                     double[] beta) {
-            this.weightedJacobian = weightedJacobian;
-            this.permutation = permutation;
-            this.rank = rank;
-            this.diagR = diagR;
-            this.jacNorm = jacNorm;
-            this.beta = beta;
-        }
-    }
-
-    /**
-     * Determines the Levenberg-Marquardt parameter.
-     *
-     * <p>This implementation is a translation in Java of the MINPACK
-     * <a href="http://www.netlib.org/minpack/lmpar.f">lmpar</a>
-     * routine.</p>
-     * <p>This method sets the lmPar and lmDir attributes.</p>
-     * <p>The authors of the original fortran function are:</p>
-     * <ul>
-     *   <li>Argonne National Laboratory. MINPACK project. March 1980</li>
-     *   <li>Burton  S. Garbow</li>
-     *   <li>Kenneth E. Hillstrom</li>
-     *   <li>Jorge   J. More</li>
-     * </ul>
-     * <p>Luc Maisonobe did the Java translation.</p>
-     *
-     * @param qy Array containing qTy.
-     * @param delta Upper bound on the euclidean norm of diagR * lmDir.
-     * @param diag Diagonal matrix.
-     * @param internalData Data (modified in-place in this method).
-     * @param solvedCols Number of solved point.
-     * @param work1 work array
-     * @param work2 work array
-     * @param work3 work array
-     * @param lmDir the "returned" LM direction will be stored in this array.
-     * @param lmPar the value of the LM parameter from the previous iteration.
-     * @return the new LM parameter
-     */
-    private double determineLMParameter(double[] qy, double delta, double[] diag,
-                                      InternalData internalData, int solvedCols,
-                                      double[] work1, double[] work2, double[] work3,
-                                      double[] lmDir, double lmPar) {
-        final double[][] weightedJacobian = internalData.weightedJacobian;
-        final int[] permutation = internalData.permutation;
-        final int rank = internalData.rank;
-        final double[] diagR = internalData.diagR;
-
-        final int nC = weightedJacobian[0].length;
-
-        // compute and store in x the gauss-newton direction, if the
-        // jacobian is rank-deficient, obtain a least squares solution
-        for (int j = 0; j < rank; ++j) {
-            lmDir[permutation[j]] = qy[j];
-        }
-        for (int j = rank; j < nC; ++j) {
-            lmDir[permutation[j]] = 0;
-        }
-        for (int k = rank - 1; k >= 0; --k) {
-            int pk = permutation[k];
-            double ypk = lmDir[pk] / diagR[pk];
-            for (int i = 0; i < k; ++i) {
-                lmDir[permutation[i]] -= ypk * weightedJacobian[i][pk];
-            }
-            lmDir[pk] = ypk;
-        }
-
-        // evaluate the function at the origin, and test
-        // for acceptance of the Gauss-Newton direction
-        double dxNorm = 0;
-        for (int j = 0; j < solvedCols; ++j) {
-            int pj = permutation[j];
-            double s = diag[pj] * lmDir[pj];
-            work1[pj] = s;
-            dxNorm += s * s;
-        }
-        dxNorm = FastMath.sqrt(dxNorm);
-        double fp = dxNorm - delta;
-        if (fp <= 0.1 * delta) {
-            lmPar = 0;
-            return lmPar;
-        }
-
-        // if the jacobian is not rank deficient, the Newton step provides
-        // a lower bound, parl, for the zero of the function,
-        // otherwise set this bound to zero
-        double sum2;
-        double parl = 0;
-        if (rank == solvedCols) {
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                work1[pj] *= diag[pj] / dxNorm;
-            }
-            sum2 = 0;
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                double sum = 0;
-                for (int i = 0; i < j; ++i) {
-                    sum += weightedJacobian[i][pj] * work1[permutation[i]];
-                }
-                double s = (work1[pj] - sum) / diagR[pj];
-                work1[pj] = s;
-                sum2 += s * s;
-            }
-            parl = fp / (delta * sum2);
-        }
-
-        // calculate an upper bound, paru, for the zero of the function
-        sum2 = 0;
-        for (int j = 0; j < solvedCols; ++j) {
-            int pj = permutation[j];
-            double sum = 0;
-            for (int i = 0; i <= j; ++i) {
-                sum += weightedJacobian[i][pj] * qy[i];
-            }
-            sum /= diag[pj];
-            sum2 += sum * sum;
-        }
-        double gNorm = FastMath.sqrt(sum2);
-        double paru = gNorm / delta;
-        if (paru == 0) {
-            paru = Precision.SAFE_MIN / FastMath.min(delta, 0.1);
-        }
-
-        // if the input par lies outside of the interval (parl,paru),
-        // set par to the closer endpoint
-        lmPar = FastMath.min(paru, FastMath.max(lmPar, parl));
-        if (lmPar == 0) {
-            lmPar = gNorm / dxNorm;
-        }
-
-        for (int countdown = 10; countdown >= 0; --countdown) {
-
-            // evaluate the function at the current value of lmPar
-            if (lmPar == 0) {
-                lmPar = FastMath.max(Precision.SAFE_MIN, 0.001 * paru);
-            }
-            double sPar = FastMath.sqrt(lmPar);
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                work1[pj] = sPar * diag[pj];
-            }
-            determineLMDirection(qy, work1, work2, internalData, solvedCols, work3, lmDir);
-
-            dxNorm = 0;
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                double s = diag[pj] * lmDir[pj];
-                work3[pj] = s;
-                dxNorm += s * s;
-            }
-            dxNorm = FastMath.sqrt(dxNorm);
-            double previousFP = fp;
-            fp = dxNorm - delta;
-
-            // if the function is small enough, accept the current value
-            // of lmPar, also test for the exceptional cases where parl is zero
-            if (FastMath.abs(fp) <= 0.1 * delta ||
-                (parl == 0 &&
-                 fp <= previousFP &&
-                 previousFP < 0)) {
-                return lmPar;
-            }
-
-            // compute the Newton correction
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                work1[pj] = work3[pj] * diag[pj] / dxNorm;
-            }
-            for (int j = 0; j < solvedCols; ++j) {
-                int pj = permutation[j];
-                work1[pj] /= work2[j];
-                double tmp = work1[pj];
-                for (int i = j + 1; i < solvedCols; ++i) {
-                    work1[permutation[i]] -= weightedJacobian[i][pj] * tmp;
-                }
-            }
-            sum2 = 0;
-            for (int j = 0; j < solvedCols; ++j) {
-                double s = work1[permutation[j]];
-                sum2 += s * s;
-            }
-            double correction = fp / (delta * sum2);
-
-            // depending on the sign of the function, update parl or paru.
-            if (fp > 0) {
-                parl = FastMath.max(parl, lmPar);
-            } else if (fp < 0) {
-                paru = FastMath.min(paru, lmPar);
-            }
-
-            // compute an improved estimate for lmPar
-            lmPar = FastMath.max(parl, lmPar + correction);
-        }
-
-        return lmPar;
-    }
-
-    /**
-     * Solve a*x = b and d*x = 0 in the least squares sense.
-     * <p>This implementation is a translation in Java of the MINPACK
-     * <a href="http://www.netlib.org/minpack/qrsolv.f">qrsolv</a>
-     * routine.</p>
-     * <p>This method sets the lmDir and lmDiag attributes.</p>
-     * <p>The authors of the original fortran function are:</p>
-     * <ul>
-     *   <li>Argonne National Laboratory. MINPACK project. March 1980</li>
-     *   <li>Burton  S. Garbow</li>
-     *   <li>Kenneth E. Hillstrom</li>
-     *   <li>Jorge   J. More</li>
-     * </ul>
-     * <p>Luc Maisonobe did the Java translation.</p>
-     *
-     * @param qy array containing qTy
-     * @param diag diagonal matrix
-     * @param lmDiag diagonal elements associated with lmDir
-     * @param internalData Data (modified in-place in this method).
-     * @param solvedCols Number of sloved point.
-     * @param work work array
-     * @param lmDir the "returned" LM direction is stored in this array
-     */
-    private void determineLMDirection(double[] qy, double[] diag,
-                                      double[] lmDiag,
-                                      InternalData internalData,
-                                      int solvedCols,
-                                      double[] work,
-                                      double[] lmDir) {
-        final int[] permutation = internalData.permutation;
-        final double[][] weightedJacobian = internalData.weightedJacobian;
-        final double[] diagR = internalData.diagR;
-
-        // copy R and Qty to preserve input and initialize s
-        //  in particular, save the diagonal elements of R in lmDir
-        for (int j = 0; j < solvedCols; ++j) {
-            int pj = permutation[j];
-            for (int i = j + 1; i < solvedCols; ++i) {
-                weightedJacobian[i][pj] = weightedJacobian[j][permutation[i]];
-            }
-            lmDir[j] = diagR[pj];
-            work[j]  = qy[j];
-        }
-
-        // eliminate the diagonal matrix d using a Givens rotation
-        for (int j = 0; j < solvedCols; ++j) {
-
-            // prepare the row of d to be eliminated, locating the
-            // diagonal element using p from the Q.R. factorization
-            int pj = permutation[j];
-            double dpj = diag[pj];
-            if (dpj != 0) {
-                Arrays.fill(lmDiag, j + 1, lmDiag.length, 0);
-            }
-            lmDiag[j] = dpj;
-
-            //  the transformations to eliminate the row of d
-            // modify only a single element of Qty
-            // beyond the first n, which is initially zero.
-            double qtbpj = 0;
-            for (int k = j; k < solvedCols; ++k) {
-                int pk = permutation[k];
-
-                // determine a Givens rotation which eliminates the
-                // appropriate element in the current row of d
-                if (lmDiag[k] != 0) {
-
-                    final double sin;
-                    final double cos;
-                    double rkk = weightedJacobian[k][pk];
-                    if (FastMath.abs(rkk) < FastMath.abs(lmDiag[k])) {
-                        final double cotan = rkk / lmDiag[k];
-                        sin   = 1.0 / FastMath.sqrt(1.0 + cotan * cotan);
-                        cos   = sin * cotan;
-                    } else {
-                        final double tan = lmDiag[k] / rkk;
-                        cos = 1.0 / FastMath.sqrt(1.0 + tan * tan);
-                        sin = cos * tan;
-                    }
-
-                    // compute the modified diagonal element of R and
-                    // the modified element of (Qty,0)
-                    weightedJacobian[k][pk] = cos * rkk + sin * lmDiag[k];
-                    final double temp = cos * work[k] + sin * qtbpj;
-                    qtbpj = -sin * work[k] + cos * qtbpj;
-                    work[k] = temp;
-
-                    // accumulate the tranformation in the row of s
-                    for (int i = k + 1; i < solvedCols; ++i) {
-                        double rik = weightedJacobian[i][pk];
-                        final double temp2 = cos * rik + sin * lmDiag[i];
-                        lmDiag[i] = -sin * rik + cos * lmDiag[i];
-                        weightedJacobian[i][pk] = temp2;
-                    }
-                }
-            }
-
-            // store the diagonal element of s and restore
-            // the corresponding diagonal element of R
-            lmDiag[j] = weightedJacobian[j][permutation[j]];
-            weightedJacobian[j][permutation[j]] = lmDir[j];
-        }
-
-        // solve the triangular system for z, if the system is
-        // singular, then obtain a least squares solution
-        int nSing = solvedCols;
-        for (int j = 0; j < solvedCols; ++j) {
-            if ((lmDiag[j] == 0) && (nSing == solvedCols)) {
-                nSing = j;
-            }
-            if (nSing < solvedCols) {
-                work[j] = 0;
-            }
-        }
-        if (nSing > 0) {
-            for (int j = nSing - 1; j >= 0; --j) {
-                int pj = permutation[j];
-                double sum = 0;
-                for (int i = j + 1; i < nSing; ++i) {
-                    sum += weightedJacobian[i][pj] * work[i];
-                }
-                work[j] = (work[j] - sum) / lmDiag[j];
-            }
-        }
-
-        // permute the components of z back to components of lmDir
-        for (int j = 0; j < lmDir.length; ++j) {
-            lmDir[permutation[j]] = work[j];
-        }
-    }
-
-    /**
-     * Decompose a matrix A as A.P = Q.R using Householder transforms.
-     * <p>As suggested in the P. Lascaux and R. Theodor book
-     * <i>Analyse num&eacute;rique matricielle appliqu&eacute;e &agrave;
-     * l'art de l'ing&eacute;nieur</i> (Masson, 1986), instead of representing
-     * the Householder transforms with u<sub>k</sub> unit vectors such that:
-     * <pre>
-     * H<sub>k</sub> = I - 2u<sub>k</sub>.u<sub>k</sub><sup>t</sup>
-     * </pre>
-     * we use <sub>k</sub> non-unit vectors such that:
-     * <pre>
-     * H<sub>k</sub> = I - beta<sub>k</sub>v<sub>k</sub>.v<sub>k</sub><sup>t</sup>
-     * </pre>
-     * where v<sub>k</sub> = a<sub>k</sub> - alpha<sub>k</sub> e<sub>k</sub>.
-     * The beta<sub>k</sub> coefficients are provided upon exit as recomputing
-     * them from the v<sub>k</sub> vectors would be costly.</p>
-     * <p>This decomposition handles rank deficient cases since the tranformations
-     * are performed in non-increasing columns norms order thanks to columns
-     * pivoting. The diagonal elements of the R matrix are therefore also in
-     * non-increasing absolute values order.</p>
-     *
-     * @param jacobian Weighted Jacobian matrix at the current point.
-     * @param solvedCols Number of solved point.
-     * @return data used in other methods of this class.
-     * @throws ConvergenceException if the decomposition cannot be performed.
-     */
-    private InternalData qrDecomposition(RealMatrix jacobian,
-                                         int solvedCols) throws ConvergenceException {
-        // Code in this class assumes that the weighted Jacobian is -(W^(1/2) J),
-        // hence the multiplication by -1.
-        final double[][] weightedJacobian = jacobian.scalarMultiply(-1).getData();
-
-        final int nR = weightedJacobian.length;
-        final int nC = weightedJacobian[0].length;
-
-        final int[] permutation = new int[nC];
-        final double[] diagR = new double[nC];
-        final double[] jacNorm = new double[nC];
-        final double[] beta = new double[nC];
-
-        // initializations
-        for (int k = 0; k < nC; ++k) {
-            permutation[k] = k;
-            double norm2 = 0;
-            for (int i = 0; i < nR; ++i) {
-                double akk = weightedJacobian[i][k];
-                norm2 += akk * akk;
-            }
-            jacNorm[k] = FastMath.sqrt(norm2);
-        }
-
-        // transform the matrix column after column
-        for (int k = 0; k < nC; ++k) {
-
-            // select the column with the greatest norm on active components
-            int nextColumn = -1;
-            double ak2 = Double.NEGATIVE_INFINITY;
-            for (int i = k; i < nC; ++i) {
-                double norm2 = 0;
-                for (int j = k; j < nR; ++j) {
-                    double aki = weightedJacobian[j][permutation[i]];
-                    norm2 += aki * aki;
-                }
-                if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
-                    throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
-                                                   nR, nC);
-                }
-                if (norm2 > ak2) {
-                    nextColumn = i;
-                    ak2        = norm2;
-                }
-            }
-            if (ak2 <= qrRankingThreshold) {
-                return new InternalData(weightedJacobian, permutation, k, diagR, jacNorm, beta);
-            }
-            int pk = permutation[nextColumn];
-            permutation[nextColumn] = permutation[k];
-            permutation[k] = pk;
-
-            // choose alpha such that Hk.u = alpha ek
-            double akk = weightedJacobian[k][pk];
-            double alpha = (akk > 0) ? -FastMath.sqrt(ak2) : FastMath.sqrt(ak2);
-            double betak = 1.0 / (ak2 - akk * alpha);
-            beta[pk] = betak;
-
-            // transform the current column
-            diagR[pk] = alpha;
-            weightedJacobian[k][pk] -= alpha;
-
-            // transform the remaining columns
-            for (int dk = nC - 1 - k; dk > 0; --dk) {
-                double gamma = 0;
-                for (int j = k; j < nR; ++j) {
-                    gamma += weightedJacobian[j][pk] * weightedJacobian[j][permutation[k + dk]];
-                }
-                gamma *= betak;
-                for (int j = k; j < nR; ++j) {
-                    weightedJacobian[j][permutation[k + dk]] -= gamma * weightedJacobian[j][pk];
-                }
-            }
-        }
-
-        return new InternalData(weightedJacobian, permutation, solvedCols, diagR, jacNorm, beta);
-    }
-
-    /**
-     * Compute the product Qt.y for some Q.R. decomposition.
-     *
-     * @param y vector to multiply (will be overwritten with the result)
-     * @param internalData Data.
-     */
-    private void qTy(double[] y,
-                     InternalData internalData) {
-        final double[][] weightedJacobian = internalData.weightedJacobian;
-        final int[] permutation = internalData.permutation;
-        final double[] beta = internalData.beta;
-
-        final int nR = weightedJacobian.length;
-        final int nC = weightedJacobian[0].length;
-
-        for (int k = 0; k < nC; ++k) {
-            int pk = permutation[k];
-            double gamma = 0;
-            for (int i = k; i < nR; ++i) {
-                gamma += weightedJacobian[i][pk] * y[i];
-            }
-            gamma *= beta[pk];
-            for (int i = k; i < nR; ++i) {
-                y[i] -= gamma * weightedJacobian[i][pk];
-            }
-        }
-    }
-}

http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/fitting/leastsquares/MultivariateJacobianFunction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math3/fitting/leastsquares/MultivariateJacobianFunction.java b/src/main/java/org/apache/commons/math3/fitting/leastsquares/MultivariateJacobianFunction.java
deleted file mode 100644
index e673855..0000000
--- a/src/main/java/org/apache/commons/math3/fitting/leastsquares/MultivariateJacobianFunction.java
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.commons.math3.fitting.leastsquares;
-
-import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.linear.RealVector;
-import org.apache.commons.math3.util.Pair;
-
-/**
- * A interface for functions that compute a vector of values and can compute their
- * derivatives (Jacobian).
- *
- * @since 3.3
- */
-public interface MultivariateJacobianFunction {
-
-    /**
-     * Compute the function value and its Jacobian.
-     *
-     * @param point the abscissae
-     * @return the values and their Jacobian of this vector valued function.
-     */
-    Pair<RealVector, RealMatrix> value(RealVector point);
-
-}


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