Comments (2)
This is failing at the gauss seidel iteration, which is guaranteed to converge only for strongly diagonally dominant matrices. IterativeSolvers gauss seidel also seems to fail:
julia> IterativeSolvers.gauss_seidel(A, b)
10000-element Vector{Float64}:
1.5800257460438275e8
-1.3554143007232122e9
3.678727322552991e9
-2.9398100121906166e9
9.609920630162582e8
-9.265191029407964e8
1.4179182468803146e9
-1.953846198176463e9
2.8147302728149977e9
-2.2543662309779e9
5.296700245710633e9
-3.518278556801988e10
7.519165127084735e11
-9.174574863473485e11
8.46692988019672e11
-4.6588672065469116e11
3.672492872009494e11
-3.434314920660273e11
2.2295164294441867e11
-2.2530896628448767e11
1.8095117462242758e11
-1.0104953470842676e11
6.047258133417195e11
-2.651110781815847e12
9.80595210606752e12
-7.792056661898067e13
2.329786496840364e13
-1.1476908757642984e13
4.62634925450458e12
-5.074849546586282e12
⋮
-Inf
Inf
-Inf
Inf
-Inf
Inf
-Inf
Inf
-Inf
Inf
-Inf
Inf
-Inf
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-Inf
Inf
-Inf
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-Inf
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from algebraicmultigrid.jl.
using LinearAlgebra: Tridiagonal
using SparseArrays: sparse
using AlgebraicMultigrid
n = 10_000
A = Tridiagonal(rand(n-1), 100*rand(n), rand(n-1))
A = A + A'
ml = ruge_stuben(sparse(A))
M = aspreconditioner(ml)
b = rand(n)
M \ b
works.
from algebraicmultigrid.jl.
Related Issues (20)
- LoadError: HOT 2
- no method matching round(::Float64, ::Int64) HOT 2
- error on Poisson system with modified diagonal HOT 1
- Unqualified overload of Base.length leads to confusing LoadErrors HOT 2
- Info about upcoming removal of packages in the General registry HOT 4
- Info about upcoming removal of packages in the General registry HOT 1
- Dependency cleanup HOT 4
- verbose HOT 1
- Hessian vector products HOT 7
- TagBot trigger issue HOT 6
- Got unsupported keyword arguments "strength", "max_levels", "max_coarse" HOT 1
- User-defined near null space HOT 3
- Move from Travis to Github Actions HOT 3
- Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning (NeurIPS 2021)
- Avoid printing numerical values of Coarse Solver when summarizing MultiLevel solver HOT 2
- Printed Grid Complexity is wrong (just copies Operator Complexity) HOT 2
- Support for CuSparse HOT 3
- Use sparse LU as a faster coarse solver than Pinv HOT 4
- Default ruge_stuben solver diverges for a Poisson problem, due to problematic interpolation HOT 3
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from algebraicmultigrid.jl.