Fix return codes for nonlinear least squares #606
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Fixes #459. The crux of the issue is that `f(x) = residual` only applies in the NonlinearProblem and SteadyStateProblem cases. When `f(x)` is a nonlinear least squares problem, finding a local minima is a solution, not a failure of the algorithm. Thus this reclassifies Stalled in NLLSQ to StalledSuccess, which makes it a successful return. Algorithms which require the NonlinearLeastSquares solution to have `||resid|| < tol` thus need to be careful with the return handling, as is done in the PR that introduces this return code SciML/SciMLBase.jl#1016. However, that's a fairly odd case because it's feasibility checking, while the normal use case for NLLSQ is for optimization, and in an optimization case there's no reason to believe you should always have a solution close to zero.
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| if SciMLBase.successful_retcode($(cache_syms[i]).retcode) && | ||
| $(cache_syms[i]).retcode != ReturnCode.StalledSuccess |
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| if SciMLBase.successful_retcode($(cache_syms[i]).retcode) && | |
| $(cache_syms[i]).retcode != ReturnCode.StalledSuccess | |
| if SciMLBase.successful_retcode($(cache_syms[i]).retcode) && | |
| $(cache_syms[i]).retcode != ReturnCode.StalledSuccess |
| @@ -146,6 +149,7 @@ end | |||
| function (cache::NonlinearTerminationModeCache)( | |||
| mode::AbstractSafeNonlinearTerminationMode, du, u, uprev, abstol, reltol, args... | |||
| ) | |||
|
|
|||
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| f(u,p) = [1.0] | ||
| nlf = NonlinearFunction(f; resid_prototype=zeros(1)) |
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| f(u,p) = [1.0] | |
| nlf = NonlinearFunction(f; resid_prototype=zeros(1)) | |
| f(u, p) = [1.0] | |
| nlf = NonlinearFunction(f; resid_prototype = zeros(1)) |
ChrisRackauckas
commented
May 12, 2025
ChrisRackauckas
commented
May 12, 2025
| sol = solve(prob) | ||
| @test SciMLBase.successful_retcode(sol) | ||
| @test sol.retcode == ReturnCode.StalledSuccess | ||
| end |
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| end | |
| end |
ChrisRackauckas
commented
May 12, 2025
ChrisRackauckas
commented
May 13, 2025
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Fixes #459. The crux of the issue is that
f(x) = residualonly applies in the NonlinearProblem and SteadyStateProblem cases. Whenf(x)is a nonlinear least squares problem, finding a local minima is a solution, not a failure of the algorithm. Thus this reclassifies Stalled in NLLSQ to StalledSuccess, which makes it a successful return.Algorithms which require the NonlinearLeastSquares solution to have
||resid|| < tolthus need to be careful with the return handling, as is done in the PR that introduces this return code SciML/SciMLBase.jl#1016. However, that's a fairly odd case because it's feasibility checking, while the normal use case for NLLSQ is for optimization, and in an optimization case there's no reason to believe you should always have a solution close to zero.