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
non-square matrix A and with non-null 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 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.
|
RealVector solve(RealVector b)
The A matrix is implicit, it is provided by the underlying decomposition algorithm.
b
- right-hand 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
- right-hand 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 © 2017 CNES. All rights reserved.