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Defaults for Banded Matrices
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avik-pal committed Oct 16, 2023
1 parent 0843b53 commit f2330a6
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5 changes: 4 additions & 1 deletion Project.toml
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@@ -1,7 +1,7 @@
name = "LinearSolve"
uuid = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
authors = ["SciML"]
version = "2.10.0"
version = "2.11.0"

[deps]
ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
Expand Down Expand Up @@ -31,6 +31,7 @@ SuiteSparse = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"

[weakdeps]
BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
BlockDiagonals = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0"
Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
Expand All @@ -42,6 +43,7 @@ Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
Pardiso = "46dd5b70-b6fb-5a00-ae2d-e8fea33afaf2"

[extensions]
LinearSolveBandedMatricesExt = "BandedMatrices"
LinearSolveBlockDiagonalsExt = "BlockDiagonals"
LinearSolveCUDAExt = "CUDA"
LinearSolveEnzymeExt = "Enzyme"
Expand All @@ -54,6 +56,7 @@ LinearSolvePardisoExt = "Pardiso"

[compat]
ArrayInterface = "7.4.11"
BandedMatrices = "1"
BlockDiagonals = "0.1"
ConcreteStructs = "0.2"
DocStringExtensions = "0.8, 0.9"
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38 changes: 38 additions & 0 deletions ext/LinearSolveBandedMatricesExt.jl
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module LinearSolveBandedMatricesExt

using BandedMatrices, LinearAlgebra, LinearSolve
import LinearSolve: defaultalg,
do_factorization, init_cacheval, DefaultLinearSolver, DefaultAlgorithmChoice

# Defaults for BandedMatrices
function defaultalg(A::BandedMatrix, b, ::OperatorAssumptions)
return DefaultLinearSolver(DefaultAlgorithmChoice.QRFactorization)

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end

function defaultalg(A::Symmetric{<:Number, <:BandedMatrix}, b, ::OperatorAssumptions)
return DefaultLinearSolver(DefaultAlgorithmChoice.CholeskyFactorization)

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end

# BandedMatrices `qr` doesn't allow other args without causing an ambiguity
do_factorization(alg::QRFactorization, A::BandedMatrix, b, u) = alg.inplace ? qr!(A) : qr(A)

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function do_factorization(alg::LUFactorization, A::BandedMatrix, b, u)
_pivot = alg.pivot isa NoPivot ? Val(false) : Val(true)
return lu!(A, _pivot; check = false)

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end

# Only init for `qr`, `ldlt` and `cholesky`
for alg in (:SVDFactorization, :LUFactorization, :MKLLUFactorization,
:DiagonalFactorization, :SparspakFactorization, :KLUFactorization,
:UMFPACKFactorization, :GenericLUFactorization, :RFLUFactorization,
:BunchKaufmanFactorization, :CHOLMODFactorization, :NormalCholeskyFactorization,
:AppleAccelerateLUFactorization)
@eval begin
function init_cacheval(::$(alg), ::BandedMatrix, b, u, Pl, Pr, maxiters::Int,

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abstol, reltol, verbose::Bool, assumptions::OperatorAssumptions)
return nothing

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end
end
end

end
2 changes: 1 addition & 1 deletion src/common.jl
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Expand Up @@ -14,7 +14,7 @@ However, in practice this computation is very expensive and thus not possible fo
Therefore, OperatorCondition lets one share to LinearSolve the expected conditioning. The higher the
expected condition number, the safer the algorithm needs to be and thus there is a trade-off between
numerical performance and stability. By default the method assumes the operator may be ill-conditioned
for the standard linear solvers to converge (such as LU-factorization), though more extreme
for the standard linear solvers to converge (such as LU-factorization), though more extreme
ill-conditioning or well-conditioning could be the case and specified through this assumption.
"""
EnumX.@enumx OperatorCondition begin
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2 changes: 1 addition & 1 deletion src/default.jl
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Expand Up @@ -163,7 +163,7 @@ function defaultalg(A, b, assump::OperatorAssumptions)
DefaultAlgorithmChoice.GenericLUFactorization
elseif VERSION >= v"1.8" && appleaccelerate_isavailable()
DefaultAlgorithmChoice.AppleAccelerateLUFactorization
elseif (length(b) <= 100 || (isopenblas() && length(b) <= 500) ||
elseif (length(b) <= 100 || (isopenblas() && length(b) <= 500) ||
(usemkl && length(b) <= 200)) &&
(A === nothing ? eltype(b) <: Union{Float32, Float64} :
eltype(A) <: Union{Float32, Float64})
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