Add chebyquad#414
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…gration - Make Cheby function robust for AD and floating-point noise - Add all necessary chebyquad overloads for type=Val(T), type=T, positional and keyword arguments, and model selection - Ensure chebyquad works for both NLP and NLS forms - Add fallback for keyword-only calls - Confirmed gradient is valid and tests now run to completion
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Pull request overview
Adds the Moré–Garbow–Hillstrom “chebyquad” test problem (Problem 35) to the OptimizationProblems.jl catalog, providing both a JuMP (PureJuMP) and AD-based (ADNLPProblems) implementation plus metadata for discovery via OptimizationProblems.meta.
Changes:
- Added
chebyquadimplementation insrc/PureJuMP/chebyquad.jl. - Added
chebyquadNLP/NLS implementations insrc/ADNLPProblems/chebyquad.jl(withuse_nlsswitch). - Added metadata and size getters in
src/Meta/chebyquad.jl.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 8 comments.
| File | Description |
|---|---|
| src/PureJuMP/chebyquad.jl | Introduces a JuMP nonlinear objective formulation for chebyquad. |
| src/ADNLPProblems/chebyquad.jl | Introduces AD-based NLP + in-place NLS residual formulations for chebyquad. |
| src/Meta/chebyquad.jl | Registers chebyquad metadata and getter functions (including NLS residual count). |
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…T) for typed constants and builds x0 from a typed step, which keeps Float32 inputs and the objective result in Float32 without forcing constructor conversions.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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## main #414 +/- ##
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- Coverage 99.80% 54.42% -45.38%
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Files 1084 1141 +57
Lines 12319 13425 +1106
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tmigot
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Apr 6, 2026
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Just a small comments. Have you tried solving both with Ipopt ?
To move a JuMP model to NLPModel you have to use NLPModelsJuMP.jl with the function MathOptNLPModel
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On checking |
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Also @tmigot |
tmigot
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Apr 6, 2026
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@tmigot Tangi I think this is done too. |
tmigot
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Jun 19, 2026
| # In case register signature or availability differs, fall back silently. | ||
| end | ||
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| @NLobjective( |
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We use the more recent JuMP API that no longer use NLobjective
| try | ||
| JuMP.register(nlp, :cheby, 2, _cheby_recurrence; autodiff = true) | ||
| catch | ||
| # In case register signature or availability differs, fall back silently. |
| kwargs..., | ||
| ) where {T} | ||
| m = max(m, n) | ||
| function F!(r, x; n = length(x), m = length(r), chebyshev = chebyshev) |
| return chebyquad(Val(model); kwargs...) | ||
| end | ||
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| function Cheby(xj, i::Integer) |
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I would suggest a name like _cheby_recurrence and this should be uniform with the jump implementation
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| get_chebyquad_nvar(; n::Integer = default_nvar, kwargs...) = n | ||
| get_chebyquad_ncon(; n::Integer = default_nvar, kwargs...) = 0 | ||
| get_chebyquad_nlin(; n::Integer = default_nvar, kwargs...) = 0 | ||
| get_chebyquad_nnln(; n::Integer = default_nvar, kwargs...) = 0 | ||
| get_chebyquad_nequ(; n::Integer = default_nvar, kwargs...) = 0 | ||
| get_chebyquad_nineq(; n::Integer = default_nvar, kwargs...) = 0 | ||
| get_chebyquad_nls_nequ(; n::Integer = default_nvar, m::Int = n, kwargs...) = max(m, n) |
| :best_known_upper_bound => 500.0, | ||
| :is_feasible => true, | ||
| :defined_everywhere => missing, | ||
| :origin => :unknown, |
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Can you add the new fields ? Take example from other problems with the same testset
| :objtype => :least_squares, | ||
| :contype => :unconstrained, | ||
| :best_known_lower_bound => -Inf, | ||
| :best_known_upper_bound => 500.0, |
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Closes #115
Builds on #280