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## org.apache.commons.math.linear
Interface DecompositionSolver

All Known Implementing Classes:
  CholeskyDecompositionImpl.Solver, EigenDecompositionImpl.Solver, LUDecompositionImpl.Solver, QRDecompositionImpl.Solver, SingularValueDecompositionImpl.Solver

    public interface 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 `LUDecomposition` can only find the solution for square 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
Version:
  $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $

Method Summary
`RealMatrix` | `getInverse()` Get the inverse (or pseudo-inverse) of the decomposed matrix.
`boolean` | `isNonSingular()` Check if the decomposed matrix is non-singular.
`double[]` | `solve(double[] b)` Solve the linear equation A × X = B for matrices A.
`RealMatrix` | `solve(RealMatrix b)` Solve the linear equation A × X = B for matrices A.
`RealVector` | `solve(RealVector b)` Solve the linear equation A × X = B for matrices A.

Method Detail

### solve

    double[] solve(double[] b)
                   throws IllegalArgumentException,
                          InvalidMatrixException

  Solve the linear equation A × X = B for matrices A.

  The A matrix is implicit, it is provided by the underlying decomposition algorithm.

  Parameters:
    `b` - right-hand side of the equation A × X = B
  Returns:
    a vector X that minimizes the two norm of A × X - B
  Throws:
    `IllegalArgumentException` - if matrices dimensions don't match
    `InvalidMatrixException` - if decomposed matrix is singular

### solve

    RealVector solve(RealVector b)
                     throws IllegalArgumentException,
                            InvalidMatrixException

  Solve the linear equation A × X = B for matrices A.

  The A matrix is implicit, it is provided by the underlying decomposition algorithm.

  Parameters:
    `b` - right-hand side of the equation A × X = B
  Returns:
    a vector X that minimizes the two norm of A × X - B
  Throws:
    `IllegalArgumentException` - if matrices dimensions don't match
    `InvalidMatrixException` - if decomposed matrix is singular

### solve

    RealMatrix solve(RealMatrix b)
                     throws IllegalArgumentException,
                            InvalidMatrixException

  Solve the linear equation A × X = B for matrices A.

  The A matrix is implicit, it is provided by the underlying decomposition algorithm.

  Parameters:
    `b` - right-hand side of the equation A × X = B
  Returns:
    a matrix X that minimizes the two norm of A × X - B
  Throws:
    `IllegalArgumentException` - if matrices dimensions don't match
    `InvalidMatrixException` - if decomposed matrix is singular

### isNonSingular

    boolean isNonSingular()

  Check if the decomposed matrix is non-singular.

  Returns:
    true if the decomposed matrix is non-singular

### getInverse

    RealMatrix getInverse()
                          throws InvalidMatrixException

  Get the inverse (or pseudo-inverse) of the decomposed matrix.

  Returns:
    inverse matrix
  Throws:
    `InvalidMatrixException` - if decomposed matrix is singular

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