Fix the tests

Author: avik-pal

src/nlsolve/trustRegion.jl

@@ -96,7 +96,6 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::SimpleTrustRegion, args.
         fₖ₊₁ = norm(fx)^2 / T(2)
 
         # Compute the ratio of the actual to predicted reduction.
-        # @show size(H), size(δ)
         @bb Hδ = H × vec(δ)
         r = (fₖ₊₁ - fₖ) / (dot(δ', g) + dot(δ', Hδ) / T(2))
 

test/23_test_problems.jl

@@ -1,4 +1,4 @@
-using SimpleNonlinearSolve, LinearAlgebra, NonlinearProblemLibrary, Test
+using SimpleNonlinearSolve, LinearAlgebra, NonlinearProblemLibrary, DiffEqBase, Test
 
 problems = NonlinearProblemLibrary.problems
 dicts = NonlinearProblemLibrary.dicts
@@ -23,7 +23,8 @@ function test_on_library(problems, dicts, alg_ops, broken_tests, ϵ = 1e-4;
                     end
                     broken = idx in broken_tests[alg] ? true : false
                     @test norm(res)≤ϵ broken=broken
-                catch
+                catch e
+                    @error e
                     broken = idx in broken_tests[alg] ? true : false
                     if broken
                         @test false broken=true
@@ -69,7 +70,7 @@ end
     alg_ops = (SimpleBroyden(),)
 
     broken_tests = Dict(alg => Int[] for alg in alg_ops)
-    broken_tests[alg_ops[1]] = [1, 2, 4, 5, 6, 11, 12, 13, 14]
+    broken_tests[alg_ops[1]] = [1, 4, 5, 6, 11, 12, 13, 14]
 
     skip_tests = Dict(alg => Int[] for alg in alg_ops)
     skip_tests[alg_ops[1]] = [22]

test/basictests.jl

@@ -156,7 +156,6 @@ end
         u0 in (1.0, [1.0, 1.0], @SVector[1.0, 1.0])
 
         probN = NonlinearProblem(quadratic_f, u0, 2.0)
-        @show solve(probN, SimpleHalley(); termination_condition).u
         @test all(solve(probN, SimpleHalley(); termination_condition).u .≈ sqrt(2.0))
     end
 end
@@ -301,6 +300,7 @@ end
 end
 
 @testset "Tolerance Tests Interval Methods: $(alg)" for alg in (Bisection(), Falsi(), ITP())
+    tspan = (1.0, 20.0)
     probB = IntervalNonlinearProblem(quadratic_f, tspan, 2.0)
     tols = [0.1, 0.01, 0.001, 0.0001, 1e-5, 1e-6, 1e-7]
     ϵ = eps(1.0) #least possible tol for all methods
@@ -313,6 +313,7 @@ end
 end
 
 @testset "Tolerance Tests Interval Methods: $(alg)" for alg in (Ridder(), Brent())
+    tspan = (1.0, 20.0)
     probB = IntervalNonlinearProblem(quadratic_f, tspan, 2.0)
     tols = [0.1] # Ridder and Brent converge rapidly so as we lower tolerance below 0.01, it converges with max precision to the solution
     ϵ = eps(1.0) #least possible tol for all methods

test/matrix_resizing_tests.jl

@@ -8,5 +8,5 @@ prob = NonlinearProblem(ff, u0, p)
 
 @testset "$(alg)" for alg in (SimpleKlement(), SimpleBroyden(), SimpleNewtonRaphson(),
     SimpleDFSane(), SimpleLimitedMemoryBroyden(), SimpleTrustRegion())
-    @test vec(solve(prob, alg).u) == solve(vecprob, alg).u
+    @test vec(solve(prob, alg).u) ≈ solve(vecprob, alg).u
 end