jvp init value + some tests

Author: yash-rs

src/trustRegion.jl

@@ -304,11 +304,15 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::TrustRegion,
       p3 = convert(eltype(u), 6.0) # c6
       p4 = convert(eltype(u), 0.0)
       if iip
-        J = ForwardDiff.jacobian(f, fu, u)
+        auto_jacvec!(g, (fu, x) -> f(fu, x, p), u, fu)
       else
-        J = ForwardDiff.jacobian(f, u)
+        if isa(u, Number)
+          g = ForwardDiff.derivative(x -> f(x, p), u)
+        else
+          g = auto_jacvec(x -> f(x, p), u, fu)
+        end
       end
-      initial_trust_radius = convert(eltype(u), p1 * norm(J * fu))
+      initial_trust_radius = convert(eltype(u), p1 * norm(g))
     elseif radius_update_scheme === RadiusUpdateSchemes.Fan
       step_threshold = convert(eltype(u), 0.0001)
       shrink_threshold = convert(eltype(u), 0.25)
@@ -527,7 +531,7 @@ function jvp!(cache::TrustRegionCache{true})
   if isa(u, Number)
     return value_derivative(x -> f(x, p), u)
   end
-  return auto_jacvec!(g, (fu, x) -> f(fu, x, p), u, fu)
+  auto_jacvec!(g, (fu, x) -> f(fu, x, p), u, fu)
   g
 end
 

test/basictests.jl

@@ -352,6 +352,22 @@ end
 @test gnewton(p) ≈ [sqrt(p[2] / p[1])]
 @test ForwardDiff.jacobian(gnewton, p) ≈ ForwardDiff.jacobian(t, p)
 
+gnewton = function (p)
+    probN = NonlinearProblem{false}(f, 0.5, p)
+    sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Yuan))
+    return [sol.u]
+end
+@test gnewton(p) ≈ [sqrt(p[2] / p[1])]
+@test ForwardDiff.jacobian(gnewton, p) ≈ ForwardDiff.jacobian(t, p)
+
+gnewton = function (p)
+    probN = NonlinearProblem{false}(f, 0.5, p)
+    sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Fan))
+    return [sol.u]
+end
+@test gnewton(p) ≈ [sqrt(p[2] / p[1])]
+@test ForwardDiff.jacobian(gnewton, p) ≈ ForwardDiff.jacobian(t, p)
+
 # Iterator interface
 f = (u, p) -> u * u - p
 g = function (p_range)
@@ -396,6 +412,9 @@ probN = NonlinearProblem(f, u0)
 @test solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Yuan)).u[end] ≈ sqrt(2.0)
 @test solve(probN, TrustRegion(; radius_update_scheme = RadiusUpdateSchemes.Yuan, autodiff = false)).u[end] ≈ sqrt(2.0)
 
+@test solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Fan)).u[end] ≈ sqrt(2.0)
+@test solve(probN, TrustRegion(; radius_update_scheme = RadiusUpdateSchemes.Fan, autodiff = false)).u[end] ≈ sqrt(2.0)
+
 for u0 in [1.0, [1, 1.0]]
     local f, probN, sol
     f = (u, p) -> u .* u .- 2.0
@@ -428,6 +447,16 @@ u = g(p)
 f(u, p)
 @test all(abs.(f(u, p)) .< 1e-10)
 
+g = function (p)
+    probN = NonlinearProblem{false}(f, u0, p)
+    sol = solve(probN, TrustRegion(radius_update_scheme = RadiusUpdateSchemes.Fan), abstol = 1e-10)
+    return sol.u
+end
+p = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+u = g(p)
+f(u, p)
+@test all(abs.(f(u, p)) .< 1e-10)
+
 # Test kwars in `TrustRegion`
 max_trust_radius = [10.0, 100.0, 1000.0]
 initial_trust_radius = [10.0, 1.0, 0.1]