diff --git a/chapter4/solvers.ipynb b/chapter4/solvers.ipynb
index 49eb5937..57af23d5 100644
--- a/chapter4/solvers.ipynb
+++ b/chapter4/solvers.ipynb
@@ -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\"`)."
    ]
   },
diff --git a/chapter4/solvers.py b/chapter4/solvers.py
index 8b7deb0a..6553c4ea 100644
--- a/chapter4/solvers.py
+++ b/chapter4/solvers.py
@@ -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
@@ -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,