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docs: use AlgebraicMultigrid's automatic (SPQR) coarse solver #871

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ChrisRackauckas merged 1 commit into from
Jun 16, 2026
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docs: use AlgebraicMultigrid's automatic (SPQR) coarse solver #871

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2 changes: 1 addition & 1 deletion docs/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -90,7 +90,7 @@ DiffEqDocs = {path = ".."}

[compat]
ADTypes = "1.7"
AlgebraicMultigrid = "0.5, 0.6, 1, 2"
AlgebraicMultigrid = "2"
BSON = "0.3"
BVProblemLibrary = "0.1.2"
BenchmarkTools = "1"
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8 changes: 2 additions & 6 deletions docs/src/tutorials/advanced_ode_example.md
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Expand Up @@ -274,10 +274,7 @@ which is more automatic. The setup is very similar to before:
import AlgebraicMultigrid
function algebraicmultigrid(W, p)
A = convert(AbstractMatrix, W)
# Use a direct (LU) coarse solver: AlgebraicMultigrid's default `Pinv` coarse solver
# forms a dense pseudo-inverse via SVD, which errors on the non-SPD `W = I - γJ`.
Pl = AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(A;
coarse_solver = AlgebraicMultigrid.LinearSolveWrapper(LS.UMFPACKFactorization())))
Pl = AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(A))
Pl, LinearAlgebra.I
end

Expand All @@ -294,8 +291,7 @@ function algebraicmultigrid2(W, p)
A = convert(AbstractMatrix, W)
Pl = AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(A,
presmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1))),
postsmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1))),
coarse_solver = AlgebraicMultigrid.LinearSolveWrapper(LS.UMFPACKFactorization())))
postsmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1)))))
Pl, LinearAlgebra.I
end

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