Add chebyquad by arnavk23 · Pull Request #414 · JuliaSmoothOptimizers/OptimizationProblems.jl

arnavk23 · 2026-03-31T07:07:32Z

Closes #115
Builds on #280

…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

arnavk23 · 2026-03-31T07:29:56Z

OptimizationProblems.jl> julia --project -e "using ADNLPModels, OptimizationProblems.ADNLPProblems, NLPModels; nlp = chebyquad(); x = get_x0(nlp); println(grad(nlp, x))"

[0.4884343917032366, 0.3427897511394064, 0.12791614745429375, -0.04864916711127345, -0.11538430033659702, -0.07476947610247212, 0.009731027758558827, 0.0643710938139633, 0.05515576651091031, 0.002857933486413361, -0.04294916891897461, -0.046556537370172665, -0.011654108898887901, 0.027366416958388744, 0.037289518364351924, 0.013153208329055744, -0.02080031789049889, -0.03448661127266264, -0.018054507407989973, 0.01144198481365151, 0.02694757406002861, 0.015734899274417083, -0.010381745365069962, -0.027054133241672582, -0.019873546920724607, 0.0031758577269359514, 0.020035663202020465, 0.015362580900101817, -0.005589643709327231, -0.02257820030185312, -0.01980318868636321, -0.000705354956114449, 0.01577153986669283, 0.01368632657181804, -0.004514535514698286, -0.020638528674029447, -0.018839492485096854, -0.0013590352625745313, 0.01379629574999258, 0.011115293172347638, -0.006541290586890774, -0.020800877590383498, -0.016833282943028813, 0.000961515104431679, 0.013370242932792443, 0.006828884473594402, -0.011667438022023515, -0.021802457728230383, -0.01227679629171297, 0.006148985919863728, 0.012256626597793075, -0.0013869419774942384, -0.01915253912920277, -0.020009502174905262, -0.002711990522957443, 0.011707142757530166, 0.0049745356697277684, -0.015151393002800896, -0.023313899457658938, -0.008591623775488187, 0.010022525841194573, 0.007208241193833812, -0.014275552760777806, -0.025259886928948032, -0.009713554930765023, 0.01071628944778388, 0.0054994507644348335, -0.019175637539573808, -0.026309655390087314, -0.0036917698538219404, 0.013432588737551378, -0.004371334136517044, -0.02960995436482998, -0.018811865226317497, 0.01111789585726821, 0.005665065477686967, -0.0286238323204927, -0.027390333954238337, 0.010042427674828854, 0.008055771748754643, -0.034478916011086866, -0.027108455996210524, 0.018628181856769038, -0.0048239968178649065, -0.04921133132577815, -0.0008755768031082747, 0.019718489087599274, -0.05432023624291835, -0.015679414679670472, 0.031133475646888842, -0.07414494351288908, 0.010005144563976252, 0.0023477253483035598, -0.08839780208559837, 0.0841272983192187, -0.16796474155652863, 0.15584154363914918, -0.18432770471192625, -0.4108940312064887, 0.623128954648702]

Copilot

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 chebyquad implementation in src/PureJuMP/chebyquad.jl.
Added chebyquad NLP/NLS implementations in src/ADNLPProblems/chebyquad.jl (with use_nls switch).
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>

codecov · 2026-04-06T14:48:00Z

Codecov Report

❌ Patch coverage is 75.92593% with 13 lines in your changes missing coverage. Please review.
✅ Project coverage is 54.42%. Comparing base (2ada450) to head (2c3a49e).
⚠️ Report is 47 commits behind head on main.

Files with missing lines	Patch %	Lines
src/PureJuMP/chebyquad.jl	0.00%	7 Missing ⚠️
src/Meta/chebyquad.jl	42.85%	4 Missing ⚠️
src/ADNLPProblems/chebyquad.jl	95.00%	2 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##             main     #414       +/-   ##
===========================================
- Coverage   99.80%   54.42%   -45.38%     
===========================================
  Files        1084     1141       +57     
  Lines       12319    13425     +1106     
===========================================
- Hits        12295     7307     -4988     
- Misses         24     6118     +6094

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tmigot

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

arnavk23 · 2026-04-06T15:50:56Z

On checking

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
 Ipopt is released as open source code under the Eclipse Public License (EPL).
         For more information visit https://github.com/coin-or/Ipopt
******************************************************************************

This is Ipopt version 3.14.19, running with linear solver MUMPS 5.8.2.

Number of nonzeros in equality constraint Jacobian...:        0
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:       55

Total number of variables............................:       10
                     variables with only lower bounds:        0
                variables with lower and upper bounds:        0
                     variables with only upper bounds:        0
Total number of equality constraints.................:        0
Total number of inequality constraints...............:        0
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
   0  3.4417194e-01 0.00e+00 6.13e-01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1  3.3500739e-01 0.00e+00 5.46e-01  -1.7 5.46e-03   2.0 1.00e+00 1.00e+00f  1
   2  3.1562894e-01 0.00e+00 4.66e-01  -1.7 1.39e-02   1.5 1.00e+00 1.00e+00f  1
   3  2.8125330e-01 0.00e+00 3.97e-01  -1.7 3.30e-02   1.0 1.00e+00 1.00e+00f  1
   4  2.3071433e-01 0.00e+00 1.96e+00  -1.7 9.43e-02   0.6 1.00e+00 1.00e+00f  1
   5  2.0433470e-01 0.00e+00 3.32e-01  -1.7 2.45e-02   1.0 1.00e+00 1.00e+00f  1
   6  1.7901036e-01 0.00e+00 1.90e-01  -1.7 5.57e-02   0.5 1.00e+00 1.00e+00f  1
   7  1.3758790e-01 0.00e+00 1.98e-01  -2.5 1.39e-01   0.0 1.00e+00 1.00e+00f  1
   8  1.2400900e-01 0.00e+00 1.41e-01  -2.5 4.66e-02   0.5 1.00e+00 1.00e+00f  1
   9  9.8455232e-02 0.00e+00 8.81e-02  -2.5 8.78e-02  -0.0 1.00e+00 1.00e+00f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  10  8.2416138e-02 0.00e+00 1.69e+00  -2.5 2.28e-01  -0.5 1.00e+00 1.00e+00f  1
  11  6.4045114e-02 0.00e+00 4.49e-01  -2.5 8.05e-02   0.8 1.00e+00 1.00e+00f  1
  12  5.0060554e-02 0.00e+00 5.56e-02  -2.5 4.16e-02   0.4 1.00e+00 1.00e+00f  1
  13  3.8872763e-02 0.00e+00 8.40e-02  -2.5 7.59e-02  -0.1 1.00e+00 1.00e+00f  1
  14  1.9745637e-02 0.00e+00 1.07e-01  -2.5 1.20e-01  -0.6 1.00e+00 1.00e+00f  1
  15  1.4290093e-02 0.00e+00 3.11e-02  -2.5 5.10e-02  -0.2 1.00e+00 1.00e+00f  1
  16  9.0105595e-03 0.00e+00 2.84e-02  -2.5 7.91e-02  -0.6 1.00e+00 1.00e+00f  1
  17  5.3129139e-03 0.00e+00 8.65e-02  -2.5 1.22e-01  -1.1 1.00e+00 1.00e+00f  1
  18  3.4492398e-03 0.00e+00 2.39e-02  -2.5 1.63e-02   0.2 1.00e+00 1.00e+00f  1
  19  2.9417370e-03 0.00e+00 5.98e-03  -3.8 1.29e-02  -0.3 1.00e+00 1.00e+00f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  20  2.5902034e-03 0.00e+00 2.15e-02  -3.8 1.03e-01    -  1.00e+00 5.00e-01f  2
  21  2.4215958e-03 0.00e+00 4.16e-03  -3.8 2.32e-02    -  1.00e+00 1.00e+00f  1
  22  2.3897931e-03 0.00e+00 1.15e-03  -3.8 1.32e-02    -  1.00e+00 1.00e+00f  1
  23  2.3864441e-03 0.00e+00 1.74e-04  -5.7 4.37e-03    -  1.00e+00 1.00e+00f  1
  24  2.3863571e-03 0.00e+00 2.40e-05  -5.7 1.29e-03    -  1.00e+00 1.00e+00f  1
  25  2.3863568e-03 0.00e+00 3.85e-08  -5.7 5.01e-05    -  1.00e+00 1.00e+00f  1
  26  2.3863568e-03 0.00e+00 9.06e-13  -8.6 2.33e-07    -  1.00e+00 1.00e+00f  1

Number of Iterations....: 26

                                   (scaled)                 (unscaled)
Objective...............:   2.3863568481876764e-03    2.3863568481876764e-03
Dual infeasibility......:   9.0615709380514886e-13    9.0615709380514886e-13
Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   0.0000000000000000e+00    0.0000000000000000e+00
Overall NLP error.......:   9.0615709380514886e-13    9.0615709380514886e-13


Number of objective function evaluations             = 32
Number of objective gradient evaluations             = 27
Number of equality constraint evaluations            = 0
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 26
Total seconds in IPOPT                               = 7.031

EXIT: Optimal Solution Found.
ad_nlp_status=first_order
ad_nlp_obj=0.0023863568481876764
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.8.2.

Number of nonzeros in equality constraint Jacobian...:      110
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:       20

Total number of variables............................:       20
                     variables with only lower bounds:        0
                variables with lower and upper bounds:        0
                     variables with only upper bounds:        0
Total number of equality constraints.................:       10
Total number of inequality constraints...............:        0
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
   0  0.0000000e+00 5.91e-01 0.00e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
Warning: SOC step rejected due to evaluation error
   1r 0.0000000e+00 5.91e-01 9.99e+02  -0.2 0.00e+00    -  0.00e+00 4.77e-07R 22
   2r 2.1481591e-01 2.83e-01 2.63e+02  -0.2 4.35e+02    -  1.00e+00 1.35e-03f  1
Warning: SOC step rejected due to evaluation error
   3  2.1481580e-01 3.84e-01 4.62e-01  -1.7 1.09e+06    -  1.00e+00 2.38e-07h 23
Warning: SOC step rejected due to evaluation error
   4  2.1481580e-01 1.73e-01 4.62e-01  -1.7 1.13e+07    -  1.00e+00 1.49e-08h 27
   5  2.1296297e-01 1.83e-01 4.58e-01  -1.7 8.89e+00  -4.0 1.00e+00 7.81e-03h  8
   6  2.1173630e-01 1.96e-01 4.56e-01  -1.7 2.69e+01    -  1.00e+00 3.91e-03h  9
   7  2.0807720e-01 2.42e-01 4.49e-01  -1.7 5.62e+00  -4.5 1.00e+00 1.56e-02h  7
   8  2.0608052e-01 2.48e-01 4.46e-01  -1.7 9.00e+00    -  1.00e+00 7.81e-03h  8
   9  1.8036072e-01 2.58e-01 3.90e-01  -1.7 1.21e+00  -4.1 1.00e+00 1.25e-01h  4
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  10  1.6837801e-01 1.32e+01 3.78e-01  -1.7 3.96e+01  -2.7 1.00e+00 3.12e-02h  6
  11  1.7452110e-01 1.04e+01 2.83e-01  -1.7 2.85e-01  -1.4 1.00e+00 2.50e-01h  3
  12  1.7979564e-01 8.19e+00 2.13e-01  -1.7 2.09e-01  -0.1 1.00e+00 2.50e-01h  3
  13  1.8243528e-01 9.87e+00 6.73e-01  -1.7 1.01e+00  -0.5 1.00e+00 1.00e+00w  1
  14  1.1125795e-01 2.86e+01 1.33e+01  -1.7 5.95e-01  -0.1 1.00e+00 1.00e+00h  1
  15  1.3586537e-01 2.52e+01 1.34e+01  -1.7 8.71e-01   0.3 1.00e+00 1.25e-01h  4
  16  1.4897339e-01 2.22e+01 1.31e+01  -1.7 6.36e-01   0.7 1.00e+00 1.25e-01h  4
  17  1.6105798e-01 2.08e+01 1.34e+01  -1.7 4.62e-01   1.2 1.00e+00 6.25e-02h  5
  18  1.6723551e-01 2.02e+01 1.37e+01  -1.7 4.28e-01   1.6 1.00e+00 3.12e-02h  6
  19  1.6618187e-01 1.89e+01 1.23e+01  -1.7 2.45e+00   1.1 1.00e+00 6.25e-02h  5
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  20  4.3830050e-01 1.24e+01 1.76e+02  -1.7 3.02e-01   1.5 1.00e+00 1.00e+00h  1
  21  6.1221989e-01 9.59e+00 4.99e+03  -1.7 1.41e-01   3.8 1.00e+00 1.00e+00h  1
  22  5.7254232e-01 3.49e+00 5.48e+03  -1.7 5.93e-02   3.3 1.00e+00 1.00e+00h  1
  23  5.7270307e-01 3.10e+00 3.57e+03  -1.7 1.26e-01   4.6 1.00e+00 2.50e-01h  3
  24  5.2483458e-01 9.70e+00 2.02e+05  -1.7 2.11e-01   5.0 1.00e+00 5.00e-01h  2
  25  5.2852849e-01 3.47e+00 1.24e+05  -1.7 7.35e-02   5.5 1.00e+00 1.00e+00h  1
  26  4.7383728e-01 1.28e+00 1.48e+04  -1.7 9.48e-02   5.0 1.00e+00 1.00e+00h  1
  27  4.4232889e-01 6.57e-01 2.22e+04  -1.7 4.38e-02   5.4 1.00e+00 1.00e+00h  1
  28  3.8583131e-01 1.27e-01 7.73e+03  -1.7 5.75e-02   4.9 1.00e+00 1.00e+00h  1
  29  3.7036136e-01 9.35e-03 5.83e+03  -1.7 2.48e-02   5.4 1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  30  3.6891026e-01 3.56e-05 1.59e+03  -1.7 2.53e-03   5.8 1.00e+00 1.00e+00h  1
  31  3.6890324e-01 4.37e-10 1.74e+00  -1.7 8.33e-06   5.3 1.00e+00 1.00e+00h  1
  32  3.6889838e-01 9.06e-11 3.39e-01  -1.7 4.87e-06   4.8 1.00e+00 1.00e+00h  1
  33  3.6888380e-01 8.19e-10 3.39e-01  -1.7 1.46e-05   4.4 1.00e+00 1.00e+00h  1
  34  3.6884006e-01 7.39e-09 3.39e-01  -1.7 4.38e-05   3.9 1.00e+00 1.00e+00h  1
  35  3.6870890e-01 6.71e-08 3.39e-01  -1.7 1.32e-04   3.4 1.00e+00 1.00e+00h  1
  36  3.6831581e-01 6.21e-07 3.39e-01  -1.7 3.94e-04   2.9 1.00e+00 1.00e+00h  1
  37  3.6713999e-01 6.16e-06 3.39e-01  -1.7 1.18e-03   2.5 1.00e+00 1.00e+00h  1
  38  3.6363842e-01 9.01e-05 3.38e-01  -1.7 3.54e-03   2.0 1.00e+00 1.00e+00h  1
  39  3.6231345e-01 1.45e-05 3.43e-01  -1.7 1.35e-03   2.4 1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  40  3.5837586e-01 2.86e-04 3.41e-01  -1.7 4.02e-03   1.9 1.00e+00 1.00e+00h  1
  41  3.5684526e-01 4.29e-05 3.54e-01  -1.7 1.56e-03   2.4 1.00e+00 1.00e+00h  1
  42  3.5227158e-01 8.94e-04 3.53e-01  -1.7 4.68e-03   1.9 1.00e+00 1.00e+00h  1
  43  3.5041207e-01 8.69e-05 3.80e-01  -1.7 1.89e-03   2.3 1.00e+00 1.00e+00h  1
  44  3.4496303e-01 1.50e-03 3.74e-01  -1.7 5.57e-03   1.8 1.00e+00 1.00e+00h  1
  45  3.2639660e-01 3.18e-02 4.20e-01  -1.7 1.88e-02   1.3 1.00e+00 1.00e+00h  1
  46  2.8040790e-01 2.14e-02 3.74e-01  -1.7 5.01e-02   0.9 1.00e+00 1.00e+00h  1
  47  2.6432091e-01 1.67e-03 3.66e-01  -1.7 1.84e-02   1.3 1.00e+00 1.00e+00h  1
  48  2.2523277e-01 1.55e-02 3.25e-01  -1.7 4.90e-02   0.8 1.00e+00 1.00e+00h  1
  49  1.5055083e-01 5.67e-02 2.46e-01  -1.7 1.11e-01   0.3 1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  50  1.2776244e-01 9.28e-03 1.92e-01  -1.7 3.25e-02   0.8 1.00e+00 1.00e+00h  1
  51  9.0366490e-02 2.05e-02 1.39e-01  -2.5 7.10e-02   0.3 1.00e+00 1.00e+00h  1
  52  7.9106095e-02 3.32e-03 1.05e-01  -2.5 2.01e-02   0.7 1.00e+00 1.00e+00h  1
  53  5.9400971e-02 9.38e-03 7.47e-02  -2.5 4.28e-02   0.2 1.00e+00 1.00e+00h  1
  54  3.2136242e-02 2.29e-01 9.18e-02  -2.5 1.82e-01  -0.2 1.00e+00 1.00e+00h  1
  55  2.0007616e-02 1.21e-01 1.08e-01  -2.5 1.30e-01  -0.7 1.00e+00 1.00e+00h  1
  56  1.5917475e-02 6.57e-02 9.15e-02  -2.5 5.54e-02  -0.3 1.00e+00 1.00e+00h  1
  57  8.8052154e-03 1.23e-02 6.22e-02  -2.5 3.25e-02   0.1 1.00e+00 1.00e+00h  1
  58  2.2856685e-03 8.46e-02 2.83e-02  -2.5 1.26e-01    -  1.00e+00 1.00e+00h  1
  59  2.8765872e-03 4.73e-03 1.23e-02  -2.5 2.92e-02  -0.3 1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  60  2.6438346e-03 4.09e-03 1.29e-02  -3.8 3.69e-02  -0.8 1.00e+00 1.00e+00h  1
  61  2.4629213e-03 5.18e-03 5.45e-03  -3.8 4.48e-02    -  1.00e+00 1.00e+00h  1
  62  2.3873016e-03 2.15e-04 7.77e-05  -3.8 8.28e-03    -  1.00e+00 1.00e+00h  1
  63  2.3863475e-03 3.82e-06 5.61e-07  -5.7 6.72e-04    -  1.00e+00 1.00e+00h  1
  64  2.3863568e-03 8.84e-10 1.54e-11  -8.6 1.09e-05    -  1.00e+00 1.00e+00h  1

Number of Iterations....: 64

                                   (scaled)                 (unscaled)
Objective...............:   2.3863568439352147e-03    2.3863568439352147e-03
Dual infeasibility......:   1.5411260809172767e-11    1.5411260809172767e-11
Constraint violation....:   8.8378489943030658e-10    8.8378489943030658e-10
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   0.0000000000000000e+00    0.0000000000000000e+00
Overall NLP error.......:   8.8378489943030658e-10    8.8378489943030658e-10


Number of objective function evaluations             = 114
Number of objective gradient evaluations             = 65
Number of equality constraint evaluations            = 211
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 66
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 64
Total seconds in IPOPT                               = 4.848

EXIT: Optimal Solution Found.
ad_nls_status=first_order
ad_nls_obj=0.0023863568439352147

arnavk23 · 2026-04-06T15:52:42Z

Also @tmigot

@testset "chebyquad constructors" begin
  nlp = ADNLPProblems.chebyquad(n = 10, use_nls = false)
  @test typeof(nlp) <: ADNLPModels.ADNLPModel
  @test !isnothing(obj(nlp, nlp.meta.x0))

  nls = ADNLPProblems.chebyquad(n = 10, use_nls = true)
  @test typeof(nls) <: ADNLPModels.ADNLSModel

  Fx = similar(nls.meta.x0, nls.nls_meta.nequ)
  residual!(nls, nls.meta.x0, Fx)
  @test length(Fx) == nls.nls_meta.nequ
  @test all(isfinite, Fx)

  # Ensure both formulations are numerically consistent at x0.
  @test obj(nlp, nlp.meta.x0) ≈ obj(nls, nls.meta.x0)
end

>> using Test, NLPModels, OptimizationProblems
>> import ADNLPModels
>> include(joinpath(pwd(), "test", "test-chebyquad.jl"))

Test Summary:          | Pass  Total  Time
chebyquad constructors |    6      6  5.8s

arnavk23 · 2026-05-12T16:51:18Z

@tmigot Tangi I think this is done too.

arnavk23 added 2 commits March 31, 2026 12:35

fixing failing tests in chebyquad.jl

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correcting chebyquad.jl to remove type annotations and simplify the code

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arnavk23 and others added 3 commits April 3, 2026 01:20

Updated chebyquad.jl to avoid T(1)-style casts. The fix now uses one(…

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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.

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removing extra variables from the chebyquad problem

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arnavk23 requested a review from tmigot April 18, 2026 20:48

arnavk23 added 3 commits April 26, 2026 05:48

Update chebyquad.jl

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Update chebyquad.jl

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