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. |
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| 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 |
Compute The Pivoted Cholesky Decomposition of a Hermitian Semi-Definite matrix. Pass it the target rank of the matrix.
| Type | Intent | Optional | Attributes | Name | ||
|---|---|---|---|---|---|---|
| type(Matrix_ps), | intent(in) | :: | AMat | The matrix A, must be hermitian, positive semi-definite. |
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| type(Matrix_ps), | intent(inout) | :: | LMat | The matrix computed. |
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| integer, | intent(in) | :: | rank_in | The target rank of the matrix. |
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| type(SolverParameters_t), | intent(in), | optional | :: | solver_parameters_in | Tarameters for the solver |