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OPM, a collection of Optimization Problems in Matlab

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arxiv 2112.05636 v2 pith:4X3U7IZ7 submitted 2021-12-10 math.OC

Serge Gratton , Philippe L. Toint This is my paper · ORCID
classification math.OC
keywords optimizationcollectionmatlabproblemsadditionalalgorithmsbound-constrainedcutest
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OPM is a small collection of CUTEst unconstrained and bound-constrained nonlinear optimization problems, which can be used in Matlab for testing optimization algorithms directly (i.e. without installing additional software).

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Twin gradient method for unconstrained optimization

    math.NA 2026-07 conditional novelty 6.0 of 10

    A Twin-Step hybrid that couples two gradient paths by mutual-distance stepsizes, then switches to ABBmin when directions become collinear, improves gradient-evaluation counts over ABBmin alone.

  2. A Finite-Difference Trust-Region Method for Convexly Constrained Smooth Optimization

    math.OC 2025-10 conditional novelty 6.0 of 10

    TRFD-S reaches (L/σ ε)-approximate solutions in O(n(L/(σε))^-2), O(n(L/(σε))^-1), and O(n log((L/(σε))^-1)) function evaluations for nonconvex, convex, and P-L objectives respectively.

  3. Enhancing finite-difference based derivative-free optimization methods with machine learning

    math.OC 2025-02 conditional novelty 6.0 of 10

    A surrogate trained with Sobolev learning accelerates a finite-difference derivative-free method, with a complexity bound that improves with the average number of successful surrogate steps.

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