Merge pull request #27 from SciML/base

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 = "6.114"
 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

@@ -8,10 +8,10 @@ and static array problems.
 """
 struct Broyden <: AbstractSimpleNonlinearSolveAlgorithm end
 
-function SciMLBase.solve(prob::NonlinearProblem,
-                         alg::Broyden, args...; abstol = nothing,
-                         reltol = nothing,
-                         maxiters = 1000, kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem,
+                           alg::Broyden, args...; abstol = nothing,
+                           reltol = nothing,
+                           maxiters = 1000, kwargs...)
     f = Base.Fix2(prob.f, prob.p)
     x = float(prob.u0)
     fₙ = f(x)

src/klement.jl

@@ -8,10 +8,10 @@ This method is non-allocating on scalar problems.
 """
 struct Klement <: AbstractSimpleNonlinearSolveAlgorithm end
 
-function SciMLBase.solve(prob::NonlinearProblem,
-                         alg::Klement, args...; abstol = nothing,
-                         reltol = nothing,
-                         maxiters = 1000, kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem,
+                           alg::Klement, args...; abstol = nothing,
+                           reltol = nothing,
+                           maxiters = 1000, kwargs...)
     f = Base.Fix2(prob.f, prob.p)
     x = float(prob.u0)
     fₙ = f(x)

src/raphson.jl

@@ -36,10 +36,10 @@ struct SimpleNewtonRaphson{CS, AD, FDT} <: AbstractNewtonAlgorithm{CS, AD, FDT}
     end
 end
 
-function SciMLBase.solve(prob::NonlinearProblem,
-                         alg::SimpleNewtonRaphson, args...; abstol = nothing,
-                         reltol = nothing,
-                         maxiters = 1000, kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem,
+                           alg::SimpleNewtonRaphson, args...; abstol = nothing,
+                           reltol = nothing,
+                           maxiters = 1000, kwargs...)
     f = Base.Fix2(prob.f, prob.p)
     x = float(prob.u0)
     fx = float(prob.u0)

src/trustRegion.jl

@@ -77,10 +77,10 @@ struct TrustRegion{CS, AD, FDT} <: AbstractNewtonAlgorithm{CS, AD, FDT}
     end
 end
 
-function SciMLBase.solve(prob::NonlinearProblem,
-                         alg::TrustRegion, args...; abstol = nothing,
-                         reltol = nothing,
-                         maxiters = 1000, kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem,
+                           alg::TrustRegion, args...; abstol = nothing,
+                           reltol = nothing,
+                           maxiters = 1000, kwargs...)
     f = Base.Fix2(prob.f, prob.p)
     x = float(prob.u0)
     T = typeof(x)

test/basictests.jl

@@ -22,7 +22,9 @@ sol = benchmark_scalar(sf, csu0)
 @test sol.retcode === ReturnCode.Success
 @test sol.u * sol.u - 2 < 1e-9
 
-@test (@ballocated benchmark_scalar(sf, csu0)) == 0
+if VERSION >= v"1.7"
+    @test (@ballocated benchmark_scalar(sf, csu0)) == 0
+end
 
 # Broyden
 function benchmark_scalar(f, u0)
@@ -33,7 +35,9 @@ end
 sol = benchmark_scalar(sf, csu0)
 @test sol.retcode === ReturnCode.Success
 @test sol.u * sol.u - 2 < 1e-9
-@test (@ballocated benchmark_scalar(sf, csu0)) == 0
+if VERSION >= v"1.7"
+    @test (@ballocated benchmark_scalar(sf, csu0)) == 0
+end
 
 # Klement
 function benchmark_scalar(f, u0)
@@ -44,7 +48,9 @@ end
 sol = benchmark_scalar(sf, csu0)
 @test sol.retcode === ReturnCode.Success
 @test sol.u * sol.u - 2 < 1e-9
-@test (@ballocated benchmark_scalar(sf, csu0)) == 0
+if VERSION >= v"1.7"
+    @test (@ballocated benchmark_scalar(sf, csu0)) == 0
+end
 
 # TrustRegion
 function benchmark_scalar(f, u0)
@@ -66,7 +72,7 @@ for alg in [SimpleNewtonRaphson(), Broyden(), Klement(),
     TrustRegion(10.0)]
     g = function (p)
         probN = NonlinearProblem{false}(f, csu0, p)
-        sol = solve(probN, alg, tol = 1e-9)
+        sol = solve(probN, alg, abstol = 1e-9)
         return sol.u[end]
     end
 
@@ -137,20 +143,11 @@ f, u0 = (u, p) -> u .* u .- 2.0, @SVector[1.0, 1.0]
 probN = NonlinearProblem(f, u0)
 
 @test solve(probN, SimpleNewtonRaphson()).u[end] ≈ sqrt(2.0)
-@test solve(probN, SimpleNewtonRaphson(); immutable = false).u[end] ≈ sqrt(2.0)
-@test solve(probN, SimpleNewtonRaphson(; autodiff = false)).u[end] ≈ sqrt(2.0)
 @test solve(probN, SimpleNewtonRaphson(; autodiff = false)).u[end] ≈ sqrt(2.0)
-
 @test solve(probN, TrustRegion(10.0)).u[end] ≈ sqrt(2.0)
-@test solve(probN, TrustRegion(10.0); immutable = false).u[end] ≈ sqrt(2.0)
-@test solve(probN, TrustRegion(10.0; autodiff = false)).u[end] ≈ sqrt(2.0)
 @test solve(probN, TrustRegion(10.0; autodiff = false)).u[end] ≈ sqrt(2.0)
-
 @test solve(probN, Broyden()).u[end] ≈ sqrt(2.0)
-@test solve(probN, Broyden(); immutable = false).u[end] ≈ sqrt(2.0)
-
 @test solve(probN, Klement()).u[end] ≈ sqrt(2.0)
-@test solve(probN, Klement(); immutable = false).u[end] ≈ sqrt(2.0)
 
 for u0 in [1.0, [1, 1.0]]
     local f, probN, sol