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2 changes: 1 addition & 1 deletion docs/src/tutorials/accelerating_choices.md
Original file line number Diff line number Diff line change
Expand Up @@ -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.
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2 changes: 1 addition & 1 deletion ext/LinearSolveSparspakExt.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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)
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2 changes: 1 addition & 1 deletion src/KLU/klu.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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
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5 changes: 3 additions & 2 deletions src/iterative_wrappers.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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

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