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 FieldLUDecomposition 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 |
|---|---|
| public FieldMatrix | |
| public boolean | Returns: true if the decomposed matrix is non-singularCheck if the decomposed matrix is non-singular. |
| public FieldVector | Returns: a vector X that minimizes the two norm of A × X - Bright-hand side of the equation A × X = B b)Solve the linear equation A × X = B for matrices A. |
| public FieldMatrix | Returns: a matrix X that minimizes the two norm of A × X - Bright-hand side of the equation A × X = B b)Solve the linear equation A × X = B for matrices A. |
| getInverse | back to summary |
|---|---|
| public FieldMatrix Get the inverse (or pseudo-inverse) of the decomposed matrix.
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| isNonSingular | back to summary |
|---|---|
| public boolean isNonSingular() Check if the decomposed matrix is non-singular.
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| solve | back to summary |
|---|---|
| public FieldVector Solve the linear equation A × X = B for matrices A. The A matrix is implicit, it is provided by the underlying decomposition algorithm.
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| solve | back to summary |
|---|---|
| public FieldMatrix Solve the linear equation A × X = B for matrices A. The A matrix is implicit, it is provided by the underlying decomposition algorithm.
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