DecompositionSolver (Apache Commons Math 3.6.1 API)

```
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

Method Summary

Methods
Modifier and Type	Method and Description
`RealMatrix`	`getInverse()` Get the pseudo-inverse of the decomposed matrix.
`boolean`	`isNonSingular()` Check if the decomposed matrix is non-singular.
`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
```
RealVector solve(RealVector b)
                 throws SingularMatrixException
```
    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:
    
    DimensionMismatchException - if the matrices dimensions do not match.
    
    SingularMatrixException - if the decomposed matrix is singular.
  - solve
```
RealMatrix solve(RealMatrix b)
                 throws SingularMatrixException
```
    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:
    
    DimensionMismatchException - if the matrices dimensions do not match.
    
    SingularMatrixException - if the 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 SingularMatrixException
```
    Get the pseudo-inverse of the decomposed matrix.
    This is equal to the inverse of the decomposed matrix, if such an inverse exists.
    If no such inverse exists, then the result has properties that resemble that of an inverse.
    In particular, in this case, if the decomposed matrix is A, then the system of equations \( A x = b \) may have no solutions, or many. If it has no solutions, then the pseudo-inverse \( A^+ \) gives the "closest" solution \( z = A^+ b \), meaning \( \left \| A z - b \right \|_2 \) is minimized. If there are many solutions, then \( z = A^+ b \) is the smallest solution, meaning \( \left \| z \right \|_2 \) is minimized.
    Note however that some decompositions cannot compute a pseudo-inverse for all matrices. For example, the LUDecomposition is not defined for non-square matrices to begin with. The QRDecomposition can operate on non-square matrices, but will throw SingularMatrixException if the decomposed matrix is singular. Refer to the javadoc of specific decomposition implementations for more details.
    
    Returns:
    pseudo-inverse matrix (which is the inverse, if it exists), if the decomposition can pseudo-invert the decomposed matrix
    
    Throws:
    
    SingularMatrixException - if the decomposed matrix is singular and the decomposition can not compute a pseudo-inverse

Interface DecompositionSolver

Method Summary

Method Detail

solve

solve

isNonSingular

getInverse