Add StalledSuccess and improve return code documentation #1016
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Tested locally. |
ChrisRackauckas
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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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| # A good local minima is not a success | ||
| resid = nlsol.resid | ||
| normresid = isdefined(integrator.opts, :internalnorm) ? | ||
| integrator.opts.internalnorm(resid, t) : norm(resid) |
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| integrator.opts.internalnorm(resid, t) : norm(resid) | |
| integrator.opts.internalnorm(resid, t) : norm(resid) |
| Stalled | ||
|
|
||
| """ | ||
| ReturnCode.StalledSuccess |
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| ReturnCode.StalledSuccess | |
| ReturnCode.StalledSuccess |
| The solution process has stalled, but the stall is not considered a failure of the solver. | ||
| For example, a nonlinear optimizer may have stalled, that is its steps went to zero, which | ||
| is a valid local minima. |
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| is a valid local minima. | |
| is a valid local minima. |
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| - For nonlinear least squares optimizations, this is given for local minima which exceed | ||
| the chosen tolerance, i.e. `f(x)=resid` where `||resid||>tol` so it's not considered |
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| - For nonlinear least squares optimizations, this is given for local minima which exceed | |
| the chosen tolerance, i.e. `f(x)=resid` where `||resid||>tol` so it's not considered | |
| - For nonlinear least squares optimizations, this is given for local minima which exceed | |
| the chosen tolerance, i.e. `f(x)=resid` where `||resid||>tol` so it's not considered |
| ## Properties | ||
| - `successful_retcode` = `true` | ||
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devmotion
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| # Do not accept StalledSuccess as a solution | ||
| # A good local minima is not a success | ||
| resid = nlsol.resid | ||
| normresid = isdefined(integrator.opts, :internalnorm) ? |
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@ChrisRackauckas integrator is not defined here. This causes test errors in ModelingToolkit, e.g. https://github.com/SciML/ModelingToolkit.jl/actions/runs/14966367552/job/42037129153?pr=3624
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ehh fuck. thanks for catching this
ChrisRackauckas
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to SciML/NonlinearSolve.jl
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May 13, 2025
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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