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Merge pull request #460 from termi-official/do/update-preconditioner-…
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Update preconditioner docs
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ChrisRackauckas authored Jan 23, 2024
2 parents 328af73 + eb65943 commit fef0a40
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14 changes: 10 additions & 4 deletions docs/src/basics/Preconditioners.md
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Expand Up @@ -83,13 +83,13 @@ The following preconditioners match the interface of LinearSolve.jl.
- [Preconditioners.CholeskyPreconditioner(A, i)](https://github.com/JuliaLinearAlgebra/Preconditioners.jl):
An incomplete Cholesky preconditioner with cut-off level `i`. Requires `A` as
a `AbstractMatrix` and positive semi-definite.
- [AlgebraicMultiGrid](https://github.com/JuliaLinearAlgebra/AlgebraicMultigrid.jl):
- [AlgebraicMultigrid](https://github.com/JuliaLinearAlgebra/AlgebraicMultigrid.jl):
Implementations of the algebraic multigrid method. Must be converted to a
preconditioner via `AlgebraicMultiGrid.aspreconditioner(AlgebraicMultiGrid.precmethod(A))`.
preconditioner via `AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.precmethod(A))`.
Requires `A` as a `AbstractMatrix`. Provides the following methods:

+ `AlgebraicMultiGrid.ruge_stuben(A)`
+ `AlgebraicMultiGrid.smoothed_aggregation(A)`
+ `AlgebraicMultigrid.ruge_stuben(A)`
+ `AlgebraicMultigrid.smoothed_aggregation(A)`
- [PyAMG](https://github.com/cortner/PyAMG.jl):
Implementations of the algebraic multigrid method. Must be converted to a
preconditioner via `PyAMG.aspreconditioner(PyAMG.precmethod(A))`.
Expand All @@ -111,3 +111,9 @@ The following preconditioners match the interface of LinearSolve.jl.
preconditioners which supports distributed computing via MPI. These can be
written using the LinearSolve.jl interface choosing algorithms like `HYPRE.ILU`
and `HYPRE.BoomerAMG`.
- [KrylovPreconditioners.jl](https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/): Provides GPU-ready
preconditioners via KernelAbstractions.jl. At the time of writing the package provides the following methods:

+ Incomplete Cholesky decomposition `KrylovPreconditioners.kp_ic0(A)`
+ Incomplete LU decomposition `KrylovPreconditioners.kp_ilu0(A)`
+ Block Jacobi `KrylovPreconditioners.BlockJacobiPreconditioner(A, nblocks, device)`

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