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S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

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arxiv 2407.07812 v1 pith:KBH2SPF3 submitted 2024-07-10 math.OC cs.PF

Serge Gratton , Philippe L. Toint This is my paper · ORCID
classification math.OCcs.PF
keywords cutestfilesmatlabproblemsjuliaproblempythonadditional
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A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.

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Cited by 2 Pith papers

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

  1. Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints

    math.OC 2025-05 conditional novelty 6.0 of 10

    RA-SQP achieves optimal O(epsilon^-4) gradient and O(epsilon^-2) linear-system complexity for equality-constrained stochastic optimization, and handles general nonlinear constraints via robust subproblems.

  2. prunAdag: an adaptive pruning-aware gradient method

    math.OC 2025-02 conditional novelty 6.0 of 10

    prunAdag separates parameters into optimisable and decreasable sets, updates them with Adagrad-like rules, and provably drives the average gradient norm to zero at rate O(log(k)/sqrt(k+1)).

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