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A Julia implementation of Algorithm NCL for constrained optimization

Ding Ma, Dominique Orban et Michael A. Saunders

Rapport technique (2021)

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Abstract

Algorithm NCL is designed for general smooth optimization problems where first and second derivatives are available, including problems whose constraints may not be linearly independent at a solution (i.e., do not satisfy the LICQ). It is equivalent to the LANCELOT augmented Lagrangian method, reformulated as a short sequence of nonlinearly constrained subproblems that can be solved efficiently by IPOPT and KNITRO, with warm starts on each subproblem. We give numerical results from a Julia implementation of Algorithm NCL on tax policy models that do not satisfy the LICQ, and on nonlinear least-squares problems and general problems from the CUTEst test set.

Mots clés

constrained optimization; second derivatives; algorithm NCL; Julia

Département: Département de mathématiques et de génie industriel
Centre de recherche: GERAD - Groupe d'études et de recherche en analyse des décisions
URL de PolyPublie: https://publications.polymtl.ca/49436/
Numéro du rapport: 2021-02
URL officielle: https://www.gerad.ca/en/papers/G-2021-02
Date du dépôt: 18 avr. 2023 15:00
Dernière modification: 25 sept. 2024 16:38
Citer en APA 7: Ma, D., Orban, D., & Saunders, M. A. (2021). A Julia implementation of Algorithm NCL for constrained optimization. (Rapport technique n° 2021-02). https://www.gerad.ca/en/papers/G-2021-02

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