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Apply JuliaFormatter to fix code formatting #663

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Merged
merged 1 commit into from
Jul 31, 2025
Merged

Apply JuliaFormatter to fix code formatting #663

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14 changes: 11 additions & 3 deletions src/KLU/klu.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,8 @@ import Base: (\), size, getproperty, setproperty!, propertynames, show,
copy, eachindex, view, sortperm, unsafe_load, zeros, convert, eltype,
length, parent, stride, finalizer, Complex, complex, imag, real, map!,
summary, println, oneunit, sizeof, isdefined, setfield!, getfield,
OutOfMemoryError, ArgumentError, OverflowError, ErrorException, DimensionMismatch
OutOfMemoryError, ArgumentError, OverflowError, ErrorException,
DimensionMismatch

# Convert from 1-based to 0-based indices
function decrement!(A::AbstractArray{T}) where {T <: Integer}
Expand Down Expand Up @@ -166,6 +167,7 @@ function _free_symbolic(K::AbstractKLUFactorization{Tv, Ti}) where {Ti <: KLUITy
end

for Ti in KLUIndexTypes, Tv in KLUValueTypes

klufree = _klu_name("free_numeric", Tv, Ti)
ptr = _klu_name("numeric", :Float64, Ti)
@eval begin
Expand Down Expand Up @@ -218,6 +220,7 @@ end
# Certain sets of inputs must be non-null *together*:
# [Lp, Li, Lx], [Up, Ui, Ux], [Fp, Fi, Fx]
for Tv in KLUValueTypes, Ti in KLUIndexTypes

extract = _klu_name("extract", Tv, Ti)
sort = _klu_name("sort", Tv, Ti)
if Tv === :ComplexF64
Expand Down Expand Up @@ -436,6 +439,7 @@ function klu_analyze!(K::KLUFactorization{Tv, Ti}, P::Vector{Ti},
end

for Tv in KLUValueTypes, Ti in KLUIndexTypes

factor = _klu_name("factor", Tv, Ti)
@eval begin
function klu_factor!(
Expand Down Expand Up @@ -464,6 +468,7 @@ for Tv in KLUValueTypes, Ti in KLUIndexTypes
end

for Tv in KLUValueTypes, Ti in KLUIndexTypes

rgrowth = _klu_name("rgrowth", Tv, Ti)
rcond = _klu_name("rcond", Tv, Ti)
condest = _klu_name("condest", Tv, Ti)
Expand Down Expand Up @@ -639,6 +644,7 @@ See also: [`klu`](@ref)
klu!

for Tv in KLUValueTypes, Ti in KLUIndexTypes

refactor = _klu_name("refactor", Tv, Ti)
@eval begin
function klu!(K::KLUFactorization{$Tv, $Ti}, nzval::Vector{$Tv};
Expand Down Expand Up @@ -676,8 +682,8 @@ function klu!(K::KLUFactorization{U}, S::SparseMatrixCSC{U};
# what should happen here when check = false? This is not really a KLU error code.
K.colptr == S.colptr && K.rowval == S.rowval ||
(decrement!(K.colptr);
decrement!(K.rowval);
throw(ArgumentError("The pattern of the original matrix must match the pattern of the refactor."))
decrement!(K.rowval);
throw(ArgumentError("The pattern of the original matrix must match the pattern of the refactor."))
)
decrement!(K.colptr)
decrement!(K.rowval)
Expand Down Expand Up @@ -710,6 +716,7 @@ This function overwrites `B` with the solution `X`, for a new solution vector `X
"""
solve!
for Tv in KLUValueTypes, Ti in KLUIndexTypes

solve = _klu_name("solve", Tv, Ti)
@eval begin
function solve!(klu::AbstractKLUFactorization{$Tv, $Ti},
Expand All @@ -726,6 +733,7 @@ for Tv in KLUValueTypes, Ti in KLUIndexTypes
end

for Tv in KLUValueTypes, Ti in KLUIndexTypes

tsolve = _klu_name("tsolve", Tv, Ti)
if Tv === :ComplexF64
call = :($tsolve(
Expand Down
5 changes: 3 additions & 2 deletions src/LinearSolve.jl
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Expand Up @@ -176,9 +176,10 @@ include("adjoint.jl")
end
end

@inline function _notsuccessful(F::LinearAlgebra.QRCompactWY{T, A}) where {T,A<:GPUArraysCore.AnyGPUArray}
@inline function _notsuccessful(F::LinearAlgebra.QRCompactWY{
T, A}) where {T, A <: GPUArraysCore.AnyGPUArray}
hasmethod(LinearAlgebra.issuccess, (typeof(F),)) ?
!LinearAlgebra.issuccess(F) : false
!LinearAlgebra.issuccess(F) : false
end

@inline function _notsuccessful(F::LinearAlgebra.QRCompactWY)
Expand Down
8 changes: 4 additions & 4 deletions src/factorization.jl
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Expand Up @@ -861,8 +861,8 @@ patterns with “more structure”.
!!! note

By default, the SparseArrays.jl are implemented for efficiency by caching the
symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
unnecessary recomputation. To further reduce computational overhead, you can disable
pattern checks entirely by setting `check_pattern = false`. Note that this may error
if the sparsity pattern does change unexpectedly.
Expand All @@ -887,8 +887,8 @@ A fast sparse LU-factorization which specializes on sparsity patterns with “le
!!! note

By default, the SparseArrays.jl are implemented for efficiency by caching the
symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
symbolic factorization. If the sparsity pattern of `A` may change between solves, set `reuse_symbolic=false`.
If the pattern is assumed or known to be constant, set `reuse_symbolic=true` to avoid
unnecessary recomputation. To further reduce computational overhead, you can disable
pattern checks entirely by setting `check_pattern = false`. Note that this may error
if the sparsity pattern does change unexpectedly.
Expand Down
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