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Update petsc solver and pc links. Resolves #143 #160

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Merged
jorgensd merged 1 commit into from
Dec 6, 2023
Merged

Update petsc solver and pc links. Resolves #143 #160

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2 changes: 1 addition & 1 deletion chapter4/solvers.ipynb
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Expand Up @@ -207,7 +207,7 @@
"\n",
"For large problems, we instead need to use an iterative method which are faster and require less memory.\n",
"## Choosing a linear solver and preconditioner\n",
"As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `\"pc_type\"` to some of the other preconditioners in petsc, which you can find in the [PETSc documentation](https://www.mcs.anl.gov/petsc/documentation/linearsolvertable.html).\n",
"As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `\"pc_type\"` to some of the other preconditioners in petsc, which you can find in at [PETSc KSP solvers](https://petsc.org/release/manual/ksp/#tab-kspdefaults) and [PETSc preconditioners](https://petsc.org/release/manual/ksp/#tab-pcdefaults).\n",
"You can set any opition in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`\"ksp_atol\"`), relative tolerance (`\"ksp_rtol\"`) and maximum number of iterations (`\"ksp_max_it\"`)."
]
},
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4 changes: 2 additions & 2 deletions chapter4/solvers.py
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Expand Up @@ -6,7 +6,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.14.7
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
Expand Down Expand Up @@ -89,7 +89,7 @@ def u_ex(mod):
#
# For large problems, we instead need to use an iterative method which are faster and require less memory.
# ## Choosing a linear solver and preconditioner
# As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `"pc_type"` to some of the other preconditioners in petsc, which you can find in the [PETSc documentation](https://www.mcs.anl.gov/petsc/documentation/linearsolvertable.html).
# As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `"pc_type"` to some of the other preconditioners in petsc, which you can find in at [PETSc KSP solvers](https://petsc.org/release/manual/ksp/#tab-kspdefaults) and [PETSc preconditioners](https://petsc.org/release/manual/ksp/#tab-pcdefaults).
# You can set any opition in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`"ksp_atol"`), relative tolerance (`"ksp_rtol"`) and maximum number of iterations (`"ksp_max_it"`).

cg_problem = LinearProblem(a, L, bcs=bcs,
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