Examples of DecompositionSolver

org.apache.commons.math.linear.DecompositionSolver
Interface handling decomposition algorithms that can solve A × X = B.
Decomposition algorithms decompose an A matrix has a product of several specific matrices from which they can solve A × X = B in least squares sense: they find X such that ||A × X - B|| is minimal.

Some solvers like {@link LUDecomposition} can only find the solution forsquare matrices and when the solution is an exact linear solution, i.e. when ||A × X - B|| is exactly 0. Other solvers can also find solutions with non-square matrix A and with non-null minimal norm. If an exact linear solution exists it is also the minimal norm solution.
@version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $ @since 2.0
org.apache.commons.math3.linear.DecompositionSolver
Interface handling decomposition algorithms that can solve A × X = B.
Decomposition algorithms decompose an A matrix has a product of several specific matrices from which they can solve A × X = B in least squares sense: they find X such that ||A × X - B|| is minimal.
Some solvers like {@link LUDecomposition} can only find the solution forsquare matrices and when the solution is an exact linear solution, i.e. when ||A × X - B|| is exactly 0. Other solvers can also find solutions with non-square matrix A and with non-null minimal norm. If an exact linear solution exists it is also the minimal norm solution. @since 2.0

Examples of org.apache.commons.math3.linear.DecompositionSolver


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

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Examples of org.apache.commons.math3.linear.DecompositionSolver

            }


            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) {

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Examples of org.apache.commons.math3.linear.DecompositionSolver


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
                = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse();
    }

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Examples of org.apache.commons.math3.linear.DecompositionSolver


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

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Examples of org.apache.commons.math3.linear.DecompositionSolver


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

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Examples of org.apache.commons.math3.linear.DecompositionSolver

            }


            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) {

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Examples of org.apache.commons.math3.linear.DecompositionSolver

  public Solver getSolver(RealMatrix M) {
    if (M == null) {
      return null;
    }
    RRQRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    if (solver.isNonSingular()) {
      return new CommonsMathSolver(solver);
    }
    // Otherwise try to report apparent rank
    int apparentRank = decomposition.getRank(0.01); // Better value?
    log.warn("{} x {} matrix is near-singular (threshold {}). Add more data or decrease the value of model.features, " +

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Examples of org.apache.commons.math3.linear.DecompositionSolver

  }  


  @Override
  public boolean isNonSingular(RealMatrix M) {
    QRDecomposition decomposition = new RRQRDecomposition(M, SINGULARITY_THRESHOLD);
    DecompositionSolver solver = decomposition.getSolver();
    return solver.isNonSingular();
  }

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Examples of org.apache.commons.math3.linear.DecompositionSolver


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

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Examples of org.apache.commons.math3.linear.DecompositionSolver

            .add(measurementModel.getMeasurementNoise());


        // invert S
        // as the error covariance matrix is a symmetric positive
        // semi-definite matrix, we can use the cholesky decomposition
        DecompositionSolver solver = new CholeskyDecomposition(s).getSolver();
        RealMatrix invertedS = solver.getInverse();


        // Inn = z(k) - H * xHat(k)-
        RealVector innovation = z.subtract(measurementMatrix.operate(stateEstimation));


        // calculate gain matrix

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