Java Code Examples for org.apache.commons.math3.exception.util.LocalizedFormats#UNABLE_TO_SOLVE_SINGULAR_PROBLEM

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Example 1
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        final Pair<RealMatrix, RealVector> normalEquation =
                computeNormalMatrix(jacobian, residuals);
        final RealMatrix normal = normalEquation.getFirst();
        final RealVector jTr = normalEquation.getSecond();
        return new CholeskyDecomposition(
                normal, SINGULARITY_THRESHOLD, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(jTr);
    } catch (NonPositiveDefiniteMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}
Example 2
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        final Pair<RealMatrix, RealVector> normalEquation =
                computeNormalMatrix(jacobian, residuals);
        final RealMatrix normal = normalEquation.getFirst();
        final RealVector jTr = normalEquation.getSecond();
        return new CholeskyDecomposition(
                normal, SINGULARITY_THRESHOLD, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(jTr);
    } catch (NonPositiveDefiniteMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}
Example 3
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // Computation will be useless without a checker (see "for-loop").
    if (checker == null) {
        throw new NullArgumentException();
    }

    final double[] targetValues = getTarget();
    final int nR = targetValues.length; // Number of observed data.

    final RealMatrix weightMatrix = getWeight();
    // Diagonal of the weight matrix.
    final double[] residualsWeights = new double[nR];
    for (int i = 0; i < nR; i++) {
        residualsWeights[i] = weightMatrix.getEntry(i, i);
    }

    final double[] currentPoint = getStartPoint();
    final int nC = currentPoint.length;

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        // Value of the objective function at "currentPoint".
        final double[] currentObjective = computeObjectiveValue(currentPoint);
        final double[] currentResiduals = computeResiduals(currentObjective);
        final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
        current = new PointVectorValuePair(currentPoint, currentObjective);

        // build the linear problem
        final double[]   b = new double[nC];
        final double[][] a = new double[nC][nC];
        for (int i = 0; i < nR; ++i) {

            final double[] grad   = weightedJacobian.getRow(i);
            final double weight   = residualsWeights[i];
            final double residual = currentResiduals[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < nC; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < nC; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < nC; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < nC; ++i) {
                currentPoint[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // Check convergence.
        if (previous != null) {
            converged = checker.converged(iter, previous, current);
            if (converged) {
                cost = computeCost(currentResiduals);
                // Update (deprecated) "point" field.
                point = current.getPoint();
                return current;
            }
        }
    }
    // Must never happen.
    throw new MathInternalError();
}
Example 4
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {

    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        updateResidualsAndCost();
        updateJacobian();
        current = new PointVectorValuePair(point, objective);

        final double[] targetValues = getTargetRef();
        final double[] residualsWeights = getWeightRef();

        // build the linear problem
        final double[]   b = new double[cols];
        final double[][] a = new double[cols][cols];
        for (int i = 0; i < rows; ++i) {

            final double[] grad   = weightedResidualJacobian[i];
            final double weight   = residualsWeights[i];
            final double residual = objective[i] - targetValues[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < cols; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < cols; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < cols; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < cols; ++i) {
                point[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // check convergence
        if (checker != null) {
            if (previous != null) {
                converged = checker.converged(iter, previous, current);
            }
        }
    }
    // we have converged
    return current;
}
Example 5
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {

    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        updateResidualsAndCost();
        updateJacobian();
        current = new PointVectorValuePair(point, objective);

        final double[] targetValues = getTargetRef();
        final double[] residualsWeights = getWeightRef();

        // build the linear problem
        final double[]   b = new double[cols];
        final double[][] a = new double[cols][cols];
        for (int i = 0; i < rows; ++i) {

            final double[] grad   = weightedResidualJacobian[i];
            final double weight   = residualsWeights[i];
            final double residual = objective[i] - targetValues[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < cols; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < cols; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < cols; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < cols; ++i) {
                point[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // check convergence
        if (checker != null) {
            if (previous != null) {
                converged = checker.converged(iter, previous, current);
            }
        }
    }
    // we have converged
    return current;
}
Example 6
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // Computation will be useless without a checker (see "for-loop").
    if (checker == null) {
        throw new NullArgumentException();
    }

    final double[] targetValues = getTarget();
    final int nR = targetValues.length; // Number of observed data.

    final RealMatrix weightMatrix = getWeight();
    // Diagonal of the weight matrix.
    final double[] residualsWeights = new double[nR];
    for (int i = 0; i < nR; i++) {
        residualsWeights[i] = weightMatrix.getEntry(i, i);
    }

    final double[] currentPoint = getStartPoint();
    final int nC = currentPoint.length;

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        // Value of the objective function at "currentPoint".
        final double[] currentObjective = computeObjectiveValue(currentPoint);
        final double[] currentResiduals = computeResiduals(currentObjective);
        final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
        current = new PointVectorValuePair(currentPoint, currentObjective);

        // build the linear problem
        final double[]   b = new double[nC];
        final double[][] a = new double[nC][nC];
        for (int i = 0; i < nR; ++i) {

            final double[] grad   = weightedJacobian.getRow(i);
            final double weight   = residualsWeights[i];
            final double residual = currentResiduals[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < nC; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < nC; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < nC; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < nC; ++i) {
                currentPoint[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // Check convergence.
        if (previous != null) {
            converged = checker.converged(iter, previous, current);
            if (converged) {
                cost = computeCost(currentResiduals);
                // Update (deprecated) "point" field.
                point = current.getPoint();
                return current;
            }
        }
    }
    // Must never happen.
    throw new MathInternalError();
}
Example 7
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {

    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        updateResidualsAndCost();
        updateJacobian();
        current = new PointVectorValuePair(point, objective);

        final double[] targetValues = getTargetRef();
        final double[] residualsWeights = getWeightRef();

        // build the linear problem
        final double[]   b = new double[cols];
        final double[][] a = new double[cols][cols];
        for (int i = 0; i < rows; ++i) {

            final double[] grad   = weightedResidualJacobian[i];
            final double weight   = residualsWeights[i];
            final double residual = objective[i] - targetValues[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < cols; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < cols; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < cols; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < cols; ++i) {
                point[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // check convergence
        if (checker != null) {
            if (previous != null) {
                converged = checker.converged(iter, previous, current);
            }
        }
    }
    // we have converged
    return current;
}
Example 8
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // Computation will be useless without a checker (see "for-loop").
    if (checker == null) {
        throw new NullArgumentException();
    }

    final double[] targetValues = getTarget();
    final int nR = targetValues.length; // Number of observed data.

    final RealMatrix weightMatrix = getWeight();
    // Diagonal of the weight matrix.
    final double[] residualsWeights = new double[nR];
    for (int i = 0; i < nR; i++) {
        residualsWeights[i] = weightMatrix.getEntry(i, i);
    }

    final double[] currentPoint = getStartPoint();
    final int nC = currentPoint.length;

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        // Value of the objective function at "currentPoint".
        final double[] currentObjective = computeObjectiveValue(currentPoint);
        final double[] currentResiduals = computeResiduals(currentObjective);
        final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
        current = new PointVectorValuePair(currentPoint, currentObjective);

        // build the linear problem
        final double[]   b = new double[nC];
        final double[][] a = new double[nC][nC];
        for (int i = 0; i < nR; ++i) {

            final double[] grad   = weightedJacobian.getRow(i);
            final double weight   = residualsWeights[i];
            final double residual = currentResiduals[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < nC; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < nC; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < nC; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < nC; ++i) {
                currentPoint[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // Check convergence.
        if (previous != null) {
            converged = checker.converged(iter, previous, current);
            if (converged) {
                cost = computeCost(currentResiduals);
                // Update (deprecated) "point" field.
                point = current.getPoint();
                return current;
            }
        }
    }
    // Must never happen.
    throw new MathInternalError();
}
Example 9
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // Computation will be useless without a checker (see "for-loop").
    if (checker == null) {
        throw new NullArgumentException();
    }

    final double[] targetValues = getTarget();
    final int nR = targetValues.length; // Number of observed data.

    final RealMatrix weightMatrix = getWeight();
    // Diagonal of the weight matrix.
    final double[] residualsWeights = new double[nR];
    for (int i = 0; i < nR; i++) {
        residualsWeights[i] = weightMatrix.getEntry(i, i);
    }

    final double[] currentPoint = getStartPoint();
    final int nC = currentPoint.length;

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        // Value of the objective function at "currentPoint".
        final double[] currentObjective = computeObjectiveValue(currentPoint);
        final double[] currentResiduals = computeResiduals(currentObjective);
        final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
        current = new PointVectorValuePair(currentPoint, currentObjective);

        // build the linear problem
        final double[]   b = new double[nC];
        final double[][] a = new double[nC][nC];
        for (int i = 0; i < nR; ++i) {

            final double[] grad   = weightedJacobian.getRow(i);
            final double weight   = residualsWeights[i];
            final double residual = currentResiduals[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < nC; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < nC; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < nC; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < nC; ++i) {
                currentPoint[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // Check convergence.
        if (previous != null) {
            converged = checker.converged(iter, previous, current);
            if (converged) {
                cost = computeCost(currentResiduals);
                // Update (deprecated) "point" field.
                point = current.getPoint();
                return current;
            }
        }
    }
    // Must never happen.
    throw new MathInternalError();
}
Example 10
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
    final ConvergenceChecker<PointVectorValuePair> checker
        = getConvergenceChecker();

    // Computation will be useless without a checker (see "for-loop").
    if (checker == null) {
        throw new NullArgumentException();
    }

    final double[] targetValues = getTarget();
    final int nR = targetValues.length; // Number of observed data.

    final RealMatrix weightMatrix = getWeight();
    // Diagonal of the weight matrix.
    final double[] residualsWeights = new double[nR];
    for (int i = 0; i < nR; i++) {
        residualsWeights[i] = weightMatrix.getEntry(i, i);
    }

    final double[] currentPoint = getStartPoint();
    final int nC = currentPoint.length;

    // iterate until convergence is reached
    PointVectorValuePair current = null;
    int iter = 0;
    for (boolean converged = false; !converged;) {
        ++iter;

        // evaluate the objective function and its jacobian
        PointVectorValuePair previous = current;
        // Value of the objective function at "currentPoint".
        final double[] currentObjective = computeObjectiveValue(currentPoint);
        final double[] currentResiduals = computeResiduals(currentObjective);
        final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
        current = new PointVectorValuePair(currentPoint, currentObjective);

        // build the linear problem
        final double[]   b = new double[nC];
        final double[][] a = new double[nC][nC];
        for (int i = 0; i < nR; ++i) {

            final double[] grad   = weightedJacobian.getRow(i);
            final double weight   = residualsWeights[i];
            final double residual = currentResiduals[i];

            // compute the normal equation
            final double wr = weight * residual;
            for (int j = 0; j < nC; ++j) {
                b[j] += wr * grad[j];
            }

            // build the contribution matrix for measurement i
            for (int k = 0; k < nC; ++k) {
                double[] ak = a[k];
                double wgk = weight * grad[k];
                for (int l = 0; l < nC; ++l) {
                    ak[l] += wgk * grad[l];
                }
            }
        }

        try {
            // solve the linearized least squares problem
            RealMatrix mA = new BlockRealMatrix(a);
            DecompositionSolver solver = useLU ?
                    new LUDecomposition(mA).getSolver() :
                    new QRDecomposition(mA).getSolver();
            final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
            // update the estimated parameters
            for (int i = 0; i < nC; ++i) {
                currentPoint[i] += dX[i];
            }
        } catch (SingularMatrixException e) {
            throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
        }

        // Check convergence.
        if (previous != null) {
            converged = checker.converged(iter, previous, current);
            if (converged) {
                cost = computeCost(currentResiduals);
                // Update (deprecated) "point" field.
                point = current.getPoint();
                return current;
            }
        }
    }
    // Must never happen.
    throw new MathInternalError();
}
Example 11
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        final Pair<RealMatrix, RealVector> normalEquation =
                computeNormalMatrix(jacobian, residuals);
        final RealMatrix normal = normalEquation.getFirst();
        final RealVector jTr = normalEquation.getSecond();
        return new LUDecomposition(normal, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(jTr);
    } catch (SingularMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}
Example 12
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 3 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        final Pair<RealMatrix, RealVector> normalEquation =
                computeNormalMatrix(jacobian, residuals);
        final RealMatrix normal = normalEquation.getFirst();
        final RealVector jTr = normalEquation.getSecond();
        return new LUDecomposition(normal, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(jTr);
    } catch (SingularMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}
Example 13
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 2 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        return new QRDecomposition(jacobian, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(residuals);
    } catch (SingularMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}
Example 14
Source File: GaussNewtonOptimizer.java    From astor with GNU General Public License v2.0 2 votes vote down vote up 
@Override
protected RealVector solve(final RealMatrix jacobian,
                           final RealVector residuals) {
    try {
        return new QRDecomposition(jacobian, SINGULARITY_THRESHOLD)
                .getSolver()
                .solve(residuals);
    } catch (SingularMatrixException e) {
        throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
    }
}