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Fix return codes for nonlinear least squares #606
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Original file line number | Diff line number | Diff line change | ||
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@@ -21,6 +21,7 @@ const AbsNormModes = Union{ | |||
step_norm_trace | ||||
max_stalled_steps | ||||
u_diff_cache::uType | ||||
leastsq::Bool | ||||
end | ||||
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get_abstol(cache::NonlinearTerminationModeCache) = cache.abstol | ||||
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@@ -36,7 +37,7 @@ function update_u!!(cache::NonlinearTerminationModeCache, u) | |||
end | ||||
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function CommonSolve.init( | ||||
::AbstractNonlinearProblem, mode::AbstractNonlinearTerminationMode, du, u, | ||||
prob::AbstractNonlinearProblem, mode::AbstractNonlinearTerminationMode, du, u, | ||||
saved_value_prototype...; abstol = nothing, reltol = nothing, kwargs... | ||||
) | ||||
T = promote_type(eltype(du), eltype(u)) | ||||
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@@ -80,10 +81,12 @@ function CommonSolve.init( | |||
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length(saved_value_prototype) == 0 && (saved_value_prototype = nothing) | ||||
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leastsq = typeof(prob) <: NonlinearLeastSquaresProblem | ||||
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return NonlinearTerminationModeCache( | ||||
u_unaliased, ReturnCode.Default, abstol, reltol, best_value, mode, | ||||
initial_objective, objectives_trace, 0, saved_value_prototype, | ||||
u0_norm, step_norm_trace, max_stalled_steps, u_diff_cache | ||||
u0_norm, step_norm_trace, max_stalled_steps, u_diff_cache, leastsq | ||||
) | ||||
end | ||||
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@@ -146,6 +149,7 @@ end | |||
function (cache::NonlinearTerminationModeCache)( | ||||
mode::AbstractSafeNonlinearTerminationMode, du, u, uprev, abstol, reltol, args... | ||||
) | ||||
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||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. [JuliaFormatter] reported by reviewdog 🐶
Suggested change
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if mode isa AbsNormSafeTerminationMode || mode isa AbsNormSafeBestTerminationMode | ||||
objective = Utils.apply_norm(mode.internalnorm, du) | ||||
criteria = abstol | ||||
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@@ -195,7 +199,13 @@ function (cache::NonlinearTerminationModeCache)( | |||
min_obj, max_obj = extrema(cache.objectives_trace) | ||||
end | ||||
if min_obj < mode.min_max_factor * max_obj | ||||
cache.retcode = ReturnCode.Stalled | ||||
if cache.leastsq | ||||
# If least squares, found a local minima thus success | ||||
cache.retcode = ReturnCode.StalledSuccess | ||||
else | ||||
# Not a success if f(x)>0 and residual too high | ||||
cache.retcode = ReturnCode.Stalled | ||||
end | ||||
return true | ||||
end | ||||
end | ||||
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@@ -218,7 +228,11 @@ function (cache::NonlinearTerminationModeCache)( | |||
stalled_step = max_step_norm ≤ reltol * (max_step_norm + cache.u0_norm) | ||||
end | ||||
if stalled_step | ||||
cache.retcode = ReturnCode.Stalled | ||||
if cache.leastsq | ||||
cache.retcode = ReturnCode.StalledSuccess | ||||
else | ||||
cache.retcode = ReturnCode.Stalled | ||||
end | ||||
return true | ||||
end | ||||
end | ||||
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Original file line number | Diff line number | Diff line change | ||||||||
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@@ -427,3 +427,12 @@ end | |||||||||
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@test !(solve(nlprob, NewtonRaphson()).alg.autodiff isa AutoPolyesterForwardDiff) | ||||||||||
end | ||||||||||
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@testitem "NonlinearLeastSquares ReturnCode" tags=[:core] begin | ||||||||||
f(u,p) = [1.0] | ||||||||||
nlf = NonlinearFunction(f; resid_prototype=zeros(1)) | ||||||||||
Comment on lines
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. [JuliaFormatter] reported by reviewdog 🐶
Suggested change
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prob = NonlinearLeastSquaresProblem(nlf, [1.0]) | ||||||||||
sol = solve(prob) | ||||||||||
@test SciMLBase.successful_retcode(sol) | ||||||||||
@test sol.retcode == ReturnCode.StalledSuccess | ||||||||||
end | ||||||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. [JuliaFormatter] reported by reviewdog 🐶
Suggested change
|
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
[JuliaFormatter] reported by reviewdog 🐶