diff --git a/docs/src/tutorials/accelerating_choices.md b/docs/src/tutorials/accelerating_choices.md
index b8f0cf7f0..9816e25da 100644
--- a/docs/src/tutorials/accelerating_choices.md
+++ b/docs/src/tutorials/accelerating_choices.md
@@ -9,7 +9,7 @@
 Great, you've learned how to use LinearSolve.jl and you're using it daily,
 either directly or through other SciML libraries, and you want to improve
 your performance. How can this be done? While it might seem at first like a
-hopeless endevour, "A\b uses a BLAS library and so it's already highly optimized
+hopeless endeavour, "A\b uses a BLAS library and so it's already highly optimized
 C code", it turns out there are many factors you need to consider to squeeze out
 the last 10x of performance. And yes, it can be about a factor of 10 in some
 scenarios, so let's dive in.
diff --git a/ext/LinearSolveSparspakExt.jl b/ext/LinearSolveSparspakExt.jl
index 9cce5edc8..05b5cae29 100644
--- a/ext/LinearSolveSparspakExt.jl
+++ b/ext/LinearSolveSparspakExt.jl
@@ -17,7 +17,7 @@ function LinearSolve.init_cacheval(
 end
 
 function LinearSolve.init_cacheval(
-        ::SparspakFactorization, A, b, u, Pl, Pr, maxiters::Int, abstol,
+        ::SparspakFactorization, A::AbstractSparseMatrixCSC, b, u, Pl, Pr, maxiters::Int, abstol,
         reltol,
         verbose::Bool, assumptions::OperatorAssumptions)
     A = convert(AbstractMatrix, A)
diff --git a/src/KLU/klu.jl b/src/KLU/klu.jl
index 8220fec5f..63c68f179 100644
--- a/src/KLU/klu.jl
+++ b/src/KLU/klu.jl
@@ -57,7 +57,7 @@ function kluerror(status::Integer)
     elseif status == KLU_INVALID
         throw(ArgumentError("Invalid Status"))
     elseif status == KLU_TOO_LARGE
-        throw(OverflowError("Integer overflow has occured"))
+        throw(OverflowError("Integer overflow has occurred"))
     else
         throw(ErrorException("Unknown KLU error code: $status"))
     end
diff --git a/src/iterative_wrappers.jl b/src/iterative_wrappers.jl
index 5b3c42a8b..8352925b9 100644
--- a/src/iterative_wrappers.jl
+++ b/src/iterative_wrappers.jl
@@ -238,8 +238,9 @@ function init_cacheval(alg::KrylovJL, A, b, u, Pl, Pr, maxiters::Int, abstol, re
 end
 
 # Krylov.jl tries to init with `ArrayPartition(undef, ...)`. Avoid hitting that!
-function init_cacheval(alg::LinearSolve.KrylovJL, A, b::RecursiveArrayTools.ArrayPartition, u, Pl, Pr,
-    maxiters::Int, abstol, reltol, verbose::Bool, ::LinearSolve.OperatorAssumptions)
+function init_cacheval(
+        alg::LinearSolve.KrylovJL, A, b::RecursiveArrayTools.ArrayPartition, u, Pl, Pr,
+        maxiters::Int, abstol, reltol, verbose::Bool, ::LinearSolve.OperatorAssumptions)
     return nothing
 end