Propagate stats from MINPACK

Author: avik-pal

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "3.1.0"
+version = "3.1.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/NonlinearSolveMINPACKExt.jl

@@ -1,13 +1,14 @@
 module NonlinearSolveMINPACKExt
 
-using NonlinearSolve, SciMLBase
+using NonlinearSolve, DiffEqBase, SciMLBase
 using MINPACK
 
 function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
             NonlinearLeastSquaresProblem{uType, iip}}, alg::CMINPACK, args...;
         abstol = 1e-6, maxiters = 100000, alias_u0::Bool = false,
         termination_condition = nothing, kwargs...) where {uType, iip}
-    @assert termination_condition===nothing "CMINPACK does not support termination conditions!"
+    @assert (termination_condition ===
+             nothing)||(termination_condition isa AbsNormTerminationMode) "CMINPACK does not support termination conditions!"
 
     if prob.u0 isa Number
         u0 = [prob.u0]
@@ -57,22 +58,26 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
                 return Cint(0)
             end
         end
-        original = MINPACK.fsolve(f!, g!, u0, m; tol = abstol, show_trace, tracing, method,
-            iterations = maxiters, kwargs...)
+        original = MINPACK.fsolve(f!, g!, vec(u0), m; tol = abstol, show_trace, tracing,
+            method, iterations = maxiters, kwargs...)
     else
-        original = MINPACK.fsolve(f!, u0, m; tol = abstol, show_trace, tracing, method,
-            iterations = maxiters, kwargs...)
+        original = MINPACK.fsolve(f!, vec(u0), m; tol = abstol, show_trace, tracing,
+            method, iterations = maxiters, kwargs...)
     end
 
     u = reshape(original.x, size(u))
     resid = original.f
     # retcode = original.converged ? ReturnCode.Success : ReturnCode.Failure
     # MINPACK lies about convergence? or maybe uses some other criteria?
     # We just check for absolute tolerance on the residual
-    objective = NonlinearSolve.DEFAULT_NORM(resid)
+    objective = maximum(abs, resid)
     retcode = ifelse(objective ≤ abstol, ReturnCode.Success, ReturnCode.Failure)
 
-    return SciMLBase.build_solution(prob, alg, u, resid; retcode, original)
+    # These are only meaningful if `tracing = true`
+    stats = SciMLBase.NLStats(original.trace.f_calls, original.trace.g_calls,
+        original.trace.g_calls, original.trace.g_calls, -1)
+
+    return SciMLBase.build_solution(prob, alg, u, resid; stats, retcode, original)
 end
 
 end

ext/NonlinearSolveNLsolveExt.jl

@@ -5,7 +5,8 @@ import UnPack: @unpack
 
 function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abstol = 1e-6,
         maxiters = 1000, alias_u0::Bool = false, termination_condition = nothing, kwargs...)
-    @assert termination_condition===nothing "NLsolveJL does not support termination conditions!"
+    @assert (termination_condition ===
+             nothing)||(termination_condition isa AbsNormTerminationMode) "NLsolveJL does not support termination conditions!"
 
     if typeof(prob.u0) <: Number
         u0 = [prob.u0]
@@ -59,19 +60,20 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abst
         end
         if prob.f.jac_prototype !== nothing
             J = zero(prob.f.jac_prototype)
-            df = OnceDifferentiable(f!, g!, u0, resid, J)
+            df = OnceDifferentiable(f!, g!, vec(u0), vec(resid), J)
         else
-            df = OnceDifferentiable(f!, g!, u0, resid)
+            df = OnceDifferentiable(f!, g!, vec(u0), vec(resid))
         end
     else
-        df = OnceDifferentiable(f!, u0, resid; autodiff)
+        df = OnceDifferentiable(f!, vec(u0), vec(resid); autodiff)
     end
 
-    original = nlsolve(df, u0; ftol = abstol, iterations = maxiters, method, store_trace,
-        extended_trace, linesearch, linsolve, factor, autoscale, m, beta, show_trace)
+    original = nlsolve(df, vec(u0); ftol = abstol, iterations = maxiters, method,
+        store_trace, extended_trace, linesearch, linsolve, factor, autoscale, m, beta,
+        show_trace)
 
     u = reshape(original.zero, size(u0))
-    f!(resid, u)
+    f!(vec(resid), vec(u))
     retcode = original.x_converged || original.f_converged ? ReturnCode.Success :
               ReturnCode.Failure
     stats = SciMLBase.NLStats(original.f_calls, original.g_calls, original.g_calls,