Stabilized Optimization Via an NCL Algorithm

Ding Ma, Kenneth L. Judd, Dominique Orban, Michael A. Saunders*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

3 Citations (Scopus)

Abstract

For optimization problems involving many nonlinear inequality constraints, we extend the bound-constrained (BCL) and linearly constrained (LCL) augmented Lagrangian approaches of LANCELOT and MINOS to an algorithm that solves a sequence of nonlinearly constrained augmented Lagrangian subproblems whose nonlinear constraints satisfy the LICQ everywhere. The NCL algorithm is implemented in AMPL and tested on large instances of a tax policy model that could not be solved directly by the state-of-the-art solvers that we tested, because of singularity in the Jacobian of the active constraints. Algorithm NCL with IPOPT as subproblem solver proves to be effective, with IPOPT using second derivatives and successfully warm starting each subproblem.
Original languageEnglish
Title of host publicationNumerical Analysis and Optimization
EditorsMehiddin Al-Baali, Lucio Grandinetti, Anton Purnama
PublisherSpringer Nature
Pages173-191
ISBN (Electronic)9783319900261
ISBN (Print)9783319900254
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event4th International Conference of Numerical Analysis and Optimization (NAO-IV 2017) - Muscat, Oman
Duration: 2 Jan 20175 Jan 2017

Publication series

NameSpringer Proceedings in Mathematics & Statistics
Volume235
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference4th International Conference of Numerical Analysis and Optimization (NAO-IV 2017)
Abbreviated titleNAO-IV 2017
Country/TerritoryOman
CityMuscat
Period2/01/175/01/17

Research Keywords

  • Stabilized optimization
  • LICQ
  • Augmented Lagrangian
  • BCL NCL
  • Interior method
  • Warm start

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