make krylovdim to large, test detection; bump version

Project.toml

@@ -1,7 +1,7 @@
 name = "KrylovKit"
 uuid = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 authors = ["Jutho Haegeman"]
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/matrixfun/expintegrator.jl

@@ -154,7 +154,7 @@ function expintegrator(A, t::Number, u::Tuple, alg::Union{Lanczos,Arnoldi})
  K = length(fact)
  V = basis(fact)
 
- if K == krylovdim || β <= tol
+ if K == krylovdim || normres(fact) <= η
  Δτ = min(Δτ, τ-τ₀)
 
  # Small matrix exponential and error estimation

test/eigsolve.jl

@@ -4,7 +4,7 @@
  A = rand(T,(n,n)) .- one(T)/2
  A = (A+A')/2
  v = rand(T,(n,))
- alg = Lanczos(orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T)))
+ alg = Lanczos(orth = orth, krylovdim = 2*n, maxiter = 1, tol = 10*n*eps(real(T)))
  n1 = div(n,2)
  D1, V1, info = eigsolve(wrapop(A), wrapvec(v), n1, :SR; orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T)))
  @test KrylovKit.eigselector(A, eltype(v); orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T))) isa Lanczos
@@ -59,7 +59,7 @@ end
  @testset for orth in (cgs2, mgs2, cgsr, mgsr)
  A = rand(T,(n,n)) .- one(T)/2
  v = rand(T,(n,))
- alg = Arnoldi(orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T)))
+ alg = Arnoldi(orth = orth, krylovdim = 2*n, maxiter = 1, tol = 10*n*eps(real(T)))
  n1 = div(n,2)
  D1, V1, info1 = eigsolve(wrapop(A), wrapvec(v), n1, :SR; orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T)))
  @test KrylovKit.eigselector(A, eltype(v); orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T))) isa Arnoldi

test/factorize.jl

@@ -17,7 +17,7 @@
  @test V'*V ≈ I
  @test A*V ≈ V*H
 
- @inferred initialize!(iter, deepcopy(fact); verbosity = 1, krylovdim = 2*n)
+ @inferred initialize!(iter, deepcopy(fact); verbosity = 1)
  states = collect(Iterators.take(iter, n)) # collect tests size and eltype?
  @test rayleighquotient(last(states)) ≈ H
  end
@@ -43,7 +43,7 @@ end
  @test V'*V ≈ I
  @test A*V ≈ V*H
 
- @inferred initialize!(iter, deepcopy(fact); verbosity = 1, krylovdim = 2*n)
+ @inferred initialize!(iter, deepcopy(fact); verbosity = 1)
  states = collect(Iterators.take(iter, n)) # collect tests size and eltype?
  @test rayleighquotient(last(states)) ≈ H
  end

test/svdsolve.jl

@@ -2,7 +2,7 @@
  @testset for T in (Float32, Float64, ComplexF32, ComplexF64)
  @testset for orth in (cgs2, mgs2, cgsr, mgsr)
  A = rand(T, (n,n))
- alg = GKL(orth = orth, krylovdim = n, maxiter = 1, tol = 10*n*eps(real(T)))
+ alg = GKL(orth = orth, krylovdim = 2*n, maxiter = 1, tol = 10*n*eps(real(T)))
  S, lvecs, rvecs, info = @inferred svdsolve(wrapop(A), wrapvec(A[:,1]), n, :LR, alg)
 
  @test S ≈ svdvals(A)