MWE: using SparseArrays using LinearAlgebra a = sparse([1, 2, 3, 4], [1, 2, 3, 4], [1.0, 2.0, 3.0, 4.0], 4, 4) af = cholesky(a) b = rand(4) y = zeros(4) ldiv!(y, af, b) Results in error: ERROR: MethodError: no method matching ldiv!(::SparseArrays.CHOLMOD.Factor{Float64, Int64}, ::Vector{Float64})…

Seems like it’s just that no one has gotten around to it. The initial implementation of \ used CHOLMOD’s out-of-place API, but it looks like there is an in-place API we could use to implement ldiv!: missing `ldiv!` overload for sparse Cholesky …

You are right, Steven. Unless one does hundreds of solves… Personally I am looking for simply avoiding allocations for multiple right-hand sides. It seems the post linked to above has a decent solution, doesn’t it?

[image] PetrKryslUCSD: You are right, Steven. Unless one does hundreds of solves… If the solution allocations are 1% of the cost for a single solve (even without including the factorization cost), then they should still be 1% of the cost if you do hundreds of solves, no?

I went with in-place LU (manque the in-place Cholesky). The allocations went from 10 GB to 200 MB, and time was reduced nearly in half.

Is there no ldiv! for Choleski factorization of a sparse matrix?

General Usage

stillyslalom May 27, 2024, 11:49pm 2

Not a simple answer, but see In place Cholesky solve with zero allocation (simplical vs. supernodal)

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Is there no ldiv! for Choleski factorization of a sparse matrix?

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