Skip a few tests and add generalize eps for complex

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "2.9.0"
+version = "2.8.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/broyden.jl

@@ -8,7 +8,7 @@ An implementation of `Broyden` with reseting and line search.
 
   - `max_resets`: the maximum number of resets to perform. Defaults to `3`.
   - `reset_tolerance`: the tolerance for the reset check. Defaults to
-    `sqrt(eps(eltype(u)))`.
+    `sqrt(eps(real(eltype(u))))`.
   - `linesearch`: the line search algorithm to use. Defaults to [`LineSearch()`](@ref),
     which means that no line search is performed. Algorithms from `LineSearches.jl` can be
     used here directly, and they will be converted to the correct `LineSearch`. It is
@@ -67,7 +67,7 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::GeneralBroyde
     u = alias_u0 ? u0 : deepcopy(u0)
     fu = evaluate_f(prob, u)
     J⁻¹ = __init_identity_jacobian(u, fu)
-    reset_tolerance = alg.reset_tolerance === nothing ? sqrt(eps(eltype(u))) :
+    reset_tolerance = alg.reset_tolerance === nothing ? sqrt(eps(real(eltype(u)))) :
                       alg.reset_tolerance
     reset_check = x -> abs(x) ≤ reset_tolerance
 

src/klement.jl

@@ -142,7 +142,7 @@ function perform_step!(cache::GeneralKlementCache{true})
     mul!(cache.Jdu, J, _vec(du))
     cache.fu .= cache.fu2 .- cache.fu
     cache.fu .= _restructure(cache.fu,
-        (_vec(cache.fu) .- cache.Jdu) ./ max.(cache.Jᵀ²du, eps(T)))
+        (_vec(cache.fu) .- cache.Jdu) ./ max.(cache.Jᵀ²du, eps(real(T))))
     mul!(cache.J_cache, _vec(cache.fu), _vec(du)')
     cache.J_cache .*= J
     mul!(cache.J_cache2, cache.J_cache, J)
@@ -202,7 +202,7 @@ function perform_step!(cache::GeneralKlementCache{false})
     cache.Jdu = J * _vec(cache.du)
     cache.fu = cache.fu2 .- cache.fu
     cache.fu = _restructure(cache.fu,
-        (_vec(cache.fu) .- cache.Jdu) ./ max.(cache.Jᵀ²du, eps(T)))
+        (_vec(cache.fu) .- cache.Jdu) ./ max.(cache.Jᵀ²du, eps(real(T))))
     cache.J_cache = ((_vec(cache.fu) * _vec(cache.du)') .* J) * J
     cache.J = J .+ cache.J_cache
 

src/lbroyden.jl

@@ -8,7 +8,7 @@ An implementation of `LimitedMemoryBroyden` with reseting and line search.
 
   - `max_resets`: the maximum number of resets to perform. Defaults to `3`.
   - `reset_tolerance`: the tolerance for the reset check. Defaults to
-    `sqrt(eps(eltype(u)))`.
+    `sqrt(eps(real(eltype(u))))`.
   - `threshold`: the number of vectors to store in the low rank approximation. Defaults
     to `10`.
   - `linesearch`: the line search algorithm to use. Defaults to [`LineSearch()`](@ref),
@@ -82,7 +82,7 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::LimitedMemory
     threshold = min(alg.threshold, maxiters)
     U, Vᵀ = __init_low_rank_jacobian(u, fu, threshold)
     du = copy(fu)
-    reset_tolerance = alg.reset_tolerance === nothing ? sqrt(eps(eltype(u))) :
+    reset_tolerance = alg.reset_tolerance === nothing ? sqrt(eps(real(eltype(u)))) :
                       alg.reset_tolerance
     reset_check = x -> abs(x) ≤ reset_tolerance
 

src/utils.jl

@@ -302,7 +302,7 @@ _issingular(x::Number) = iszero(x)
     hasmethod(issingular, Tuple{T}) && return :(issingular(x))
     return :(__issingular(x))
 end
-__issingular(x::AbstractMatrix{T}) where {T} = cond(x) > inv(sqrt(eps(T)))
+__issingular(x::AbstractMatrix{T}) where {T} = cond(x) > inv(sqrt(eps(real(T))))
 __issingular(x) = false ## If SciMLOperator and such
 
 # If factorization is LU then perform that and update the linsolve cache

test/23_test_problems.jl

@@ -3,7 +3,8 @@ using NonlinearSolve, LinearAlgebra, LinearSolve, NonlinearProblemLibrary, Test
 problems = NonlinearProblemLibrary.problems
 dicts = NonlinearProblemLibrary.dicts
 
-function test_on_library(problems, dicts, alg_ops, broken_tests, ϵ = 1e-4)
+function test_on_library(problems, dicts, alg_ops, broken_tests, ϵ = 1e-4;
+        skip_tests = nothing)
     for (idx, (problem, dict)) in enumerate(zip(problems, dicts))
         x = dict["start"]
         res = similar(x)
@@ -15,6 +16,9 @@ function test_on_library(problems, dicts, alg_ops, broken_tests, ϵ = 1e-4)
                         termination_condition = AbsNormTerminationMode())
                     problem(res, sol.u, nothing)
 
+                    skip = skip_tests !== nothing && alg in keys(skip_tests) &&
+                           idx in skip_tests[alg]
+                    skip && @test_skip norm(res) ≤ ϵ
                     broken = idx in broken_tests[alg] ? true : false
                     @test norm(res)≤ϵ broken=broken
                 catch
@@ -90,6 +94,9 @@ end
     broken_tests = Dict(alg => Int[] for alg in alg_ops)
     broken_tests[alg_ops[1]] = [1, 2, 4, 5, 6, 11, 12, 13, 14]
 
+    skip_tests = Dict(alg => Int[] for alg in alg_ops)
+    skip_tests[alg_ops[1]] = [22]
+
     test_on_library(problems, dicts, alg_ops, broken_tests)
 end