Merge pull request #460 from termi-official/do/update-preconditioner-docs

Update preconditioner docs

Author: ChrisRackauckas

docs/src/basics/Preconditioners.md

@@ -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))`.
@@ -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)`