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arXiv · 2101.02164

A Julia implementation of Algorithm NCL for constrained optimization

Ding Ma·Dominique Orban·Michael A. Saunders
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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.

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Ding Ma, Dominique Orban, Michael A. Saunders. 2021-01-06. A Julia implementation of Algorithm NCL for constrained optimization. https://doi.org/10.13140/rg.2.2.29888.35841

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