Examples of org.apache.commons.math.linear.LUDecompositionImpl

org.apache.commons.math.linear.LUDecompositionImpl
Calculates the LUP-decomposition of a square matrix.
The LUP-decomposition of a matrix A consists of three matrices L, U and P that satisfy: PA = LU, L is lower triangular, and U is upper triangular and P is a permutation matrix. All matrices are m×m.

As shown by the presence of the P matrix, this decomposition is implemented using partial pivoting.
@version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 2.0


    Vector2d direction = rm.getNewDirectionVector();
    direction.normalize();
    RealMatrix coefficients = new Array2DRowRealMatrix(new double[][] { { direction.getX(), -(sideVectX) },
        { direction.getY(), -(sideVectY) } }, false);
    DecompositionSolver solver = new LUDecompositionImpl(coefficients).getSolver();
    RealVector constants = new ArrayRealVector(new double[] { sideX - roboter1.getX(), sideY - roboter1.getY() }, false);
    RealVector solution = solver.solve(constants);


    double r = solution.getEntry(0);
    // double s = solution.getEntry(1);

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     */
    @Override
    protected RealMatrix calculateBetaVariance() {
        int p = X.getColumnDimension();
        RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
        RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
        return Rinv.multiply(Rinv.transpose());
    }

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     * <p>The inverse of the covariance matrix is lazily evaluated and cached.</p>
     * @return inverse of the covariance
     */
    protected RealMatrix getOmegaInverse() {
        if (OmegaInverse == null) {
            OmegaInverse = new LUDecompositionImpl(Omega).getSolver().getInverse();
        }
        return OmegaInverse;
    }

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    @Override
    protected RealVector calculateBeta() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XT = X.transpose();
        RealMatrix XTOIX = XT.multiply(OI).multiply(X);
        RealMatrix inverse = new LUDecompositionImpl(XTOIX).getSolver().getInverse();
        return inverse.multiply(XT).multiply(OI).operate(Y);
    }

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     */
    @Override
    protected RealMatrix calculateBetaVariance() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XTOIX = X.transpose().multiply(OI).multiply(X);
        return new LUDecompositionImpl(XTOIX).getSolver().getInverse();
    }

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            }


            try {


                // solve the linearized least squares problem
                RealVector dX = new LUDecompositionImpl(a).getSolver().solve(b);


                // update the estimated parameters
                for (int i = 0; i < parameters.length; ++i) {
                    parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i));
                }

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        }


        try {
            // compute the covariances matrix
            RealMatrix inverse =
                new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
            return inverse.getData();
        } catch (InvalidMatrixException ime) {
            throw new EstimationException(LocalizedFormats.UNABLE_TO_COMPUTE_COVARIANCE_SINGULAR_PROBLEM);
        }

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        }


        try {
            // compute the covariances matrix
            RealMatrix inverse =
                new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
            return inverse.getData();
        } catch (InvalidMatrixException ime) {
            throw new OptimizationException("unable to compute covariances: singular problem");
        }

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        }


        try {
            // compute the covariance matrix
            RealMatrix inverse =
                new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
            return inverse.getData();
        } catch (InvalidMatrixException ime) {
            throw new OptimizationException(LocalizedFormats.UNABLE_TO_COMPUTE_COVARIANCE_SINGULAR_PROBLEM);
        }

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            try {


                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecompositionImpl(mA).getSolver() :
                        new QRDecompositionImpl(mA).getSolver();
                final double[] dX = solver.solve(b);


                // update the estimated parameters
                for (int i = 0; i < cols; ++i) {

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