deprecate RecursiveVec, improve x0 initialisation for GPU

.github/workflows/ci-nightly.yml

@@ -24,16 +24,14 @@ jobs:
           - 'nightly'
         os:
           - ubuntu-latest
-          - macOS-latest
-          - windows-latest
         arch:
           - x64
     steps:
       - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@latest
       - uses: julia-actions/julia-runtest@latest

.github/workflows/ci.yml

@@ -21,7 +21,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # LTS version
+          - '1.6'
+          - 'lts'
           - '1' # automatically expands to the latest stable 1.x release of Julia
         os:
           - ubuntu-latest
@@ -31,11 +32,11 @@ jobs:
           - x64
     steps:
       - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@latest
       - uses: julia-actions/julia-runtest@latest
       - uses: julia-actions/julia-processcoverage@v1
@@ -52,20 +53,19 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # LTS version
+          - 'lts'
           - '1' # automatically expands to the latest stable 1.x release of Julia
         os:
           - ubuntu-latest
-          - macOS-latest
         arch:
           - x64
     steps:
       - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@latest
       - uses: julia-actions/julia-runtest@latest
         env:

Project.toml

@@ -8,6 +8,7 @@ GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PackageExtensionCompat = "65ce6f38-6b18-4e1d-a461-8949797d7930"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 VectorInterface = "409d34a3-91d5-4945-b6ec-7529ddf182d8"
 
 [weakdeps]
@@ -27,7 +28,7 @@ PackageExtensionCompat = "1"
 Printf = "1"
 Random = "1"
 Test = "1"
-TestExtras = "0.2"
+TestExtras = "0.2,0.3"
 VectorInterface = "0.4"
 Zygote = "0.6"
 julia = "1.6"
@@ -37,10 +38,9 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestExtras = "5ed8adda-3752-4e41-b88a-e8b09835ee3a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "Aqua", "Random", "TestExtras", "ChainRulesTestUtils", "ChainRulesCore", "FiniteDifferences", "Zygote"]
+test = ["Test", "Aqua", "TestExtras", "ChainRulesTestUtils", "ChainRulesCore", "FiniteDifferences", "Zygote"]

ext/KrylovKitChainRulesCoreExt/KrylovKitChainRulesCoreExt.jl

@@ -11,6 +11,5 @@ include("utilities.jl")
 include("linsolve.jl")
 include("eigsolve.jl")
 include("svdsolve.jl")
-include("constructor.jl")
 
 end # module

ext/KrylovKitChainRulesCoreExt/eigsolve.jl

@@ -87,6 +87,7 @@ function make_eigsolve_pullback(config, f, fᴴ, x₀, howmany, which, alg_prima
                 end
             end
         end
+
         # Compute actual pullback data:
         #------------------------------
         ws = compute_eigsolve_pullback_data(Δvals, Δvecs, view(vals, 1:n), view(vecs, 1:n),

src/KrylovKit.jl

@@ -24,6 +24,7 @@ using VectorInterface
 using VectorInterface: add!!
 using LinearAlgebra
 using Printf
+using Random
 using GPUArraysCore
 using PackageExtensionCompat
 const IndexRange = AbstractRange{Int}
@@ -238,7 +239,7 @@ include("matrixfun/exponentiate.jl")
 include("matrixfun/expintegrator.jl")
 
 # custom vector types
-include("recursivevec.jl")
 include("innerproductvec.jl")
+Base.@deprecate(RecursiveVec(args...), tuple(args...))
 
 end

src/eigsolve/eigsolve.jl

@@ -177,12 +177,14 @@ EigSorter(f::F; rev=false) where {F} = EigSorter{F}(f, rev)
 
 const Selector = Union{Symbol,EigSorter}
 
+# TODO: initialize with `Random.rand!(similar(A, size(A, 1)))` for GPU arrays
 function eigsolve(A::AbstractMatrix,
                   howmany::Int=1,
                   which::Selector=:LM,
                   T::Type=eltype(A);
                   kwargs...)
-    return eigsolve(A, rand(T, size(A, 1)), howmany, which; kwargs...)
+    x₀ = Random.rand!(similar(A, T, size(A, 1)))
+    return eigsolve(A, x₀, howmany, which; kwargs...)
 end
 
 function eigsolve(f, n::Int, howmany::Int=1, which::Selector=:LM, T::Type=Float64;

src/eigsolve/geneigsolve.jl

@@ -150,21 +150,24 @@ function geneigsolve(AB::Tuple{AbstractMatrix,AbstractMatrix},
     if !(size(AB[1], 1) == size(AB[1], 2) == size(AB[2], 1) == size(AB[2], 2))
         throw(DimensionMismatch("Matrices `A` and `B` should be square and have matching size"))
     end
-    return geneigsolve(AB, rand(T, size(AB[1], 1)), howmany::Int, which; kwargs...)
+    x₀ = Random.rand!(similar(AB[1], T, size(AB[1], 1)))
+    return geneigsolve(AB, x₀, howmany::Int, which; kwargs...)
 end
 function geneigsolve(AB::Tuple{Any,AbstractMatrix},
                      howmany::Int=1,
                      which::Selector=:LM,
                      T=eltype(AB[2]);
                      kwargs...)
-    return geneigsolve(AB, rand(T, size(AB[2], 1)), howmany, which; kwargs...)
+    x₀ = Random.rand!(similar(AB[2], T, size(AB[2], 1)))
+    return geneigsolve(AB, x₀, howmany, which; kwargs...)
 end
 function geneigsolve(AB::Tuple{AbstractMatrix,Any},
                      howmany::Int=1,
                      which::Selector=:LM,
                      T=eltype(AB[1]);
                      kwargs...)
-    return geneigsolve(AB, rand(T, size(AB[1], 1)), howmany, which; kwargs...)
+    x₀ = Random.rand!(similar(AB[1], T, size(AB[1], 1)))
+    return geneigsolve(AB, x₀, howmany, which; kwargs...)
 end
 
 function geneigsolve(f,

src/eigsolve/svdsolve.jl

@@ -121,7 +121,8 @@ function svdsolve(A::AbstractMatrix,
                   which::Selector=:LR,
                   T::Type=eltype(A);
                   kwargs...)
-    return svdsolve(A, rand(T, size(A, 1)), howmany, which; kwargs...)
+    x₀ = Random.rand!(similar(A, T, size(A, 1)))
+    return svdsolve(A, x₀, howmany, which; kwargs...)
 end
 function svdsolve(f, n::Int, howmany::Int=1, which::Selector=:LR, T::Type=Float64;
                   kwargs...)