Solve the matrix equation AX = B
Solve the matrix equation AX = B using the conjugate gradient method.
| Type | Intent | Optional | Attributes | Name | ||
|---|---|---|---|---|---|---|
| type(Matrix_ps), | intent(in) | :: | AMat |
The matrix A, must be hermitian, positive definite. |
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| type(Matrix_ps), | intent(inout) | :: | XMat |
The solved for matrix X. |
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| type(Matrix_ps), | intent(in) | :: | BMat |
The right hand side. |
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| type(SolverParameters_t), | intent(in), | optional | :: | solver_parameters_in |
Parameters for the solver |
Compute The Cholesky Decomposition of a Hermitian Positive Definite matrix. This is a really naive implementation, that might be worth revisiting.
| Type | Intent | Optional | Attributes | Name | ||
|---|---|---|---|---|---|---|
| type(Matrix_ps), | intent(in) | :: | AMat |
The matrix A, must be hermitian, positive definite. |
||
| type(Matrix_ps), | intent(inout) | :: | LMat |
The lower diagonal matrix computed. |
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| type(SolverParameters_t), | intent(in), | optional | :: | solver_parameters_in |
Parameters for the solver |