public interface DecompositionSolver
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 nonsquare matrix A and with nonnull minimal norm. If an exact linear
solution exists it is also the minimal norm solution.
Modifier and Type  Method and Description 

RealMatrix 
getInverse()
Get the inverse (or pseudoinverse) of the decomposed matrix.

boolean 
isNonSingular()
Check if the decomposed matrix is nonsingular.

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.

RealVector solve(RealVector b)
The A matrix is implicit, it is provided by the underlying decomposition algorithm.
b
 righthand side of the equation A × X = BDimensionMismatchException
 if the matrices dimensions do not match.SingularMatrixException
 if the decomposed matrix is singular.RealMatrix solve(RealMatrix b)
The A matrix is implicit, it is provided by the underlying decomposition algorithm.
b
 righthand side of the equation A × X = BDimensionMismatchException
 if the matrices dimensions do not match.SingularMatrixException
 if the decomposed matrix is singular.boolean isNonSingular()
RealMatrix getInverse()
SingularMatrixException
 if the decomposed matrix is singular.Copyright © 20032012 The Apache Software Foundation. All Rights Reserved.