Merge branch 'main' into vp-ldiv

Author: vpuri3

.github/workflows/CI.yml

@@ -1,27 +1,26 @@
 name: CI
 on:
-  - push
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
 jobs:
   test:
-    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
-    runs-on: ${{ matrix.os }}
+    runs-on: ubuntu-latest
     strategy:
       matrix:
+        group:
+          - All
         version:
           - '1'
           - '1.6'
-        os:
-          - ubuntu-latest
-        arch:
-          - x64
-        group:
-          - Core
     steps:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
         with:
           version: ${{ matrix.version }}
-          arch: ${{ matrix.arch }}
       - uses: actions/cache@v1
         env:
           cache-name: cache-artifacts
@@ -38,18 +37,7 @@ jobs:
       - uses: codecov/codecov-action@v1
         with:
           file: lcov.info
-  docs:
-    name: Documentation
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@latest
+      - uses: coverallsapp/github-action@master
         with:
-          version: '1'
-      - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
-      - name: Build and deploy
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
-        run: julia --project=docs/ docs/make.jl
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          path-to-lcov: ./lcov.info

Project.toml

@@ -1,7 +1,7 @@
 name = "LinearSolve"
 uuid = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 authors = ["SciML"]
-version = "1.13.0"
+version = "1.14.0"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

docs/src/solvers/solvers.md

@@ -19,10 +19,12 @@ Pardiso.jl's methods are also known to be very efficient sparse linear solvers.
 
 As sparse matrices get larger, iterative solvers tend to get more efficient than
 factorization methods if a lower tolerance of the solution is required.
+
 IterativeSolvers.jl uses a low-rank Q update in its GMRES so it tends to be
 faster than Krylov.jl for CPU-based arrays, but it's only compatible with
 CPU-based arrays while Krylov.jl is more general and will support accelerators
-like CUDA.
+like CUDA. Krylov.jl works with CPUs and GPUs and tends to be more efficient than other
+Krylov-based methods.
 
 Finally, a user can pass a custom function ofr the linear solve using
 `LinearSolveFunction()` if existing solvers are not optimal for their application.
@@ -161,3 +163,16 @@ KrylovJL(args...; KrylovAlg = Krylov.gmres!,
                   gmres_restart=0, window=0,
                   kwargs...)
 ```
+
+### KrylovKit.jl
+
+- `KrylovKitJL_CG(args...;kwargs...)`: A generic CG implementation
+- `KrylovKitJL_GMRES(args...;kwargs...)`: A generic GMRES implementation
+
+The general algorithm is:
+
+```julia
+function KrylovKitJL(args...;
+                     KrylovAlg = KrylovKit.GMRES, gmres_restart = 0,
+                     kwargs...)
+```