Use DiffEqBase high level handling

Author: ChrisRackauckas

Project.toml

@@ -5,6 +5,7 @@ version = "0.1.5"
 
 [deps]
 ArrayInterfaceCore = "30b0a656-2188-435a-8636-2ec0e6a096e2"
+DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -15,6 +16,7 @@ StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 
 [compat]
 ArrayInterfaceCore = "0.1.1"
+DiffEqBase = "6.115"
 FiniteDiff = "2"
 ForwardDiff = "0.10.3"
 Reexport = "0.2, 1"

src/SimpleNonlinearSolve.jl

@@ -6,6 +6,7 @@ using ForwardDiff: Dual
 using StaticArraysCore
 using LinearAlgebra
 import ArrayInterfaceCore
+using DiffEqBase
 
 @reexport using SciMLBase
 
@@ -28,11 +29,11 @@ import SnoopPrecompile
 SnoopPrecompile.@precompile_all_calls begin for T in (Float32, Float64)
     prob_no_brack = NonlinearProblem{false}((u, p) -> u .* u .- p, T(0.1), T(2))
     for alg in (SimpleNewtonRaphson, Broyden, Klement)
-        solve(prob_no_brack, alg(), tol = T(1e-2))
+        solve(prob_no_brack, alg(), abstol = T(1e-2))
     end
 
     for alg in (TrustRegion(10.0),)
-        solve(prob_no_brack, alg, tol = T(1e-2))
+        solve(prob_no_brack, alg, abstol = T(1e-2))
     end
 
     #=
@@ -47,7 +48,7 @@ SnoopPrecompile.@precompile_all_calls begin for T in (Float32, Float64)
 
     prob_brack = IntervalNonlinearProblem{false}((u, p) -> u * u - p, T.((0.0, 2.0)), T(2))
     for alg in (Bisection, Falsi)
-        solve(prob_brack, alg(), tol = T(1e-2))
+        solve(prob_brack, alg(), abstol = T(1e-2))
     end
 end end
 

src/broyden.jl

@@ -8,7 +8,7 @@ and static array problems.
 """
 struct Broyden <: AbstractSimpleNonlinearSolveAlgorithm end
 
-function SciMLBase.solve(prob::NonlinearProblem,
+function SciMLBase.__solve(prob::NonlinearProblem,
                          alg::Broyden, args...; abstol = nothing,
                          reltol = nothing,
                          maxiters = 1000, kwargs...)

src/klement.jl

@@ -8,7 +8,7 @@ This method is non-allocating on scalar problems.
 """
 struct Klement <: AbstractSimpleNonlinearSolveAlgorithm end
 
-function SciMLBase.solve(prob::NonlinearProblem,
+function SciMLBase.__solve(prob::NonlinearProblem,
                          alg::Klement, args...; abstol = nothing,
                          reltol = nothing,
                          maxiters = 1000, kwargs...)

src/raphson.jl

@@ -36,7 +36,7 @@ struct SimpleNewtonRaphson{CS, AD, FDT} <: AbstractNewtonAlgorithm{CS, AD, FDT}
     end
 end
 
-function SciMLBase.solve(prob::NonlinearProblem,
+function SciMLBase.__solve(prob::NonlinearProblem,
                          alg::SimpleNewtonRaphson, args...; abstol = nothing,
                          reltol = nothing,
                          maxiters = 1000, kwargs...)

src/trustRegion.jl

@@ -77,7 +77,7 @@ struct TrustRegion{CS, AD, FDT} <: AbstractNewtonAlgorithm{CS, AD, FDT}
     end
 end
 
-function SciMLBase.solve(prob::NonlinearProblem,
+function SciMLBase.__solve(prob::NonlinearProblem,
                          alg::TrustRegion, args...; abstol = nothing,
                          reltol = nothing,
                          maxiters = 1000, kwargs...)