Merge pull request #118 from TorkelE/better_nonlinear_solve_wps

Better nonlinear solve wps

Author: ChrisRackauckas

Project.toml

@@ -17,14 +17,24 @@ SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
+BVProblemLibrary = "0.1"
+BoundaryValueDiffEq = "4, 5"
+DDEProblemLibrary = "0.1"
 DelayDiffEq = "5.20"
 DiffEqBase = "6.94.4"
 DiffEqNoiseProcess = "5.0"
 NLsolve = "4.2"
+NonlinearSolve = "1, 2"
+ODEProblemLibrary = "0.1"
+OrdinaryDiffEq = "6"
+ParameterizedFunctions = "5"
 RecipesBase = "0.7, 0.8, 1.0"
 RecursiveArrayTools = "2"
 RootedTrees = "1, 2"
+SDEProblemLibrary = "0.1"
 SciMLBase = "1.74, 2"
+StochasticDelayDiffEq = "1"
+StochasticDiffEq = "6"
 julia = "1.6"
 
 [extras]

src/benchmark.jl

@@ -372,8 +372,6 @@ end
 
 # Work precision information for a nonlinear problem.
 function WorkPrecision(prob::NonlinearProblem, alg, abstols, reltols, dts = nothing; name = nothing, appxsol = nothing, error_estimate = :l2, numruns = 20, seconds = 2, kwargs...)
-    isnothing(appxsol) && error("Must provide the real value as the \"appxsol\" kwarg.")
-
     N = length(abstols)
     errors = Vector{Float64}(undef, N)
     times = Vector{Float64}(undef, N)
@@ -394,7 +392,11 @@ function WorkPrecision(prob::NonlinearProblem, alg, abstols, reltols, dts = noth
             sol = solve(_prob, alg; kwargs..., abstol = abstols[i], reltol = reltols[i])
 
             if error_estimate == :l2
-                errors[i] = sqrt(sum(abs2, sol .- appxsol))
+                if isnothing(appxsol)
+                    errors[i] = sqrt(sum(abs2, sol.resid))                    
+                else
+                    errors[i] = sqrt(sum(abs2, sol .- appxsol))
+                end
             else
                 error("Unsupported norm used: $(error_estimate).")
             end

test/nonlinearsolve_wpdiagram_tests.jl

@@ -1,11 +1,11 @@
-# Fetch pakages.
-using NonlinearSolve, DiffEqDevTools, Plots
+# Fetch packages.
+using DiffEqDevTools, NonlinearSolve, Plots, Test
 
-# Prepares NonlinearProblem.
 let
-    f(u, p) = u .* u .- p
-    u0 = [1.0, 1.0]
-    p = 2.0
+    # Prepares NonlinearProblem.
+    f(u, p) = 3u .^3 .+ 2u .^2 .+ u + .- p
+    u0 = [1.0, 6.0]
+    p = [1.0, 3.0]
     static_prob = NonlinearProblem(f, u0, p)
     real_sol = solve(static_prob, NewtonRaphson(), reltol = 1e-15, abstol = 1e-15)
 
@@ -14,11 +14,17 @@ let
     reltols = 1.0 ./ 10.0 .^ (8:12)
     setups = [Dict(:alg=>NewtonRaphson())
             Dict(:alg=>TrustRegion())]
-    solnames = ["NewtonRaphson";"TrustRegion"]
+    solnames = ["NewtonRaphson"; "TrustRegion"]
 
     # Makes WP-diagram
     wp = WorkPrecisionSet(static_prob, abstols, reltols, setups; names=solnames, numruns=100, appxsol=real_sol, error_estimate=:l2) 
 
     # Checks that all errors are small (they definitely should be).
-    all(vcat(getfield.(wp.wps, :errors)...) .< 10e-9)
+    @test all(vcat(getfield.(wp.wps, :errors)...) .< 10e-9)
+    @test length(plot(wp).series_list) == 2
+
+    # Check without appxsol.
+    wp = WorkPrecisionSet(static_prob, abstols, reltols, setups; names=solnames, numruns=100, error_estimate=:l2) 
+    @test all(vcat(getfield.(wp.wps, :errors)...) .< 10e-9)
+    @test length(plot(wp).series_list) == 2
 end