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exp: Padé denominator via GESVFactorization #240

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exp: Padé denominator via GESVFactorization #240

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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ ForwardDiff = "0.10.13, 1"
GPUArraysCore = "0.1, 0.2"
GenericSchur = "0.5.3"
LinearAlgebra = "1.10"
LinearSolve = "4.1"
LinearSolve = "4.2"
PrecompileTools = "1"
Printf = "1.10"
Random = "1"
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23 changes: 11 additions & 12 deletions src/exp_baseexp.jl
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
using LinearSolve: LinearSolve, LinearProblem
using LinearSolve: LinearSolve, LinearProblem, GESVFactorization

"""
ExpMethodHigham2005Base()
Expand All @@ -16,15 +16,16 @@ function alloc_mem(
U = Matrix{T}(undef, n, n)
V = Matrix{T}(undef, n, n)
temp = Matrix{T}(undef, n, n)
# Cached LinearSolve workspace for the Padé denominator solves, using
# LinearSolve's default algorithm choice (size-dependent: e.g. generic LU
# for tiny matrices, LAPACK/RecursiveFactorization above). The buffers are
# workspace-owned, so aliasing lets the factorization refactorize in place
# on every `solve!` instead of copying the n×n matrix.
# Cached LinearSolve workspace for the Padé denominator solves.
# GESVFactorization mirrors the raw LAPACK.gesv! this code historically
# used (single combined factorize-and-solve call, bit-identical results)
# and benchmarks at parity with it from n = 5 through n = 250. The buffers
# are workspace-owned, so aliasing lets the factorization overwrite Abuf in
# place on every `solve!` instead of copying the n×n matrix.
Abuf = Matrix{T}(undef, n, n)
Bbuf = Matrix{T}(undef, n, n)
linsolve = LinearSolve.init(
LinearProblem(Abuf, Bbuf);
LinearProblem(Abuf, Bbuf), GESVFactorization();
alias = LinearSolve.LinearAliasSpecifier(alias_A = true, alias_b = true)
)
return (A2, P, U, V, temp, linsolve)
Expand All @@ -37,13 +38,11 @@ function _pade_linsolve!(
) where {T}
Abuf = linsolve.A
copyto!(Abuf, temp)
linsolve.A = Abuf # flag the refactorization; lu! overwrites Abuf
linsolve.A = Abuf # flag the refactorization; gesv! overwrites Abuf
copyto!(linsolve.b, X)
sol = LinearSolve.solve!(linsolve)
# The Pade denominator is nonsingular by construction for the branch norm
# bounds, so this only triggers if even the default algorithm's safety
# fallback failed; surface it like LAPACK.gesv! did rather than silently
# propagating garbage.
# LAPACK.gesv! threw on a singular denominator; keep that contract instead
# of silently propagating garbage from a failed factorization.
if !LinearSolve.SciMLBase.successful_retcode(sol.retcode)
throw(LinearAlgebra.SingularException(0))
end
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