QPALM: A Newton-type Proximal Augmented Lagrangian Method for Quadratic Programs

Ben Hermans, Andreas Themelis, Panagiotis Patrinos

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

We present a proximal augmented Lagrangian based solver for general quadratic programs (QPs), relying on semismooth Newton iterations with exact line search to solve the inner subproblems. The exact line search reduces in this case to finding the zero of a one-dimensional monotone, piecewise affine function and can be carried out very efficiently. Our algorithm requires the solution of a linear system at every iteration, but as the matrix to be factorized depends on the active constraints, efficient sparse factorization updates can be employed like in active-set methods. Both primal and dual residuals can be enforced down to strict tolerances and otherwise infeasibility can be detected from intermediate iterates. A C implementation of the proposed algorithm is tested and benchmarked against other state-of-the-art QP solvers for a large variety of problem data and shown to compare favorably against these solvers.

本文言語英語
ホスト出版物のタイトル2019 IEEE 58th Conference on Decision and Control, CDC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ4325-4330
ページ数6
ISBN(電子版)9781728113982
DOI
出版ステータス出版済み - 12 2019
イベント58th IEEE Conference on Decision and Control, CDC 2019 - Nice, フランス
継続期間: 12 11 201912 13 2019

出版物シリーズ

名前Proceedings of the IEEE Conference on Decision and Control
2019-December
ISSN(印刷版)0743-1546

会議

会議58th IEEE Conference on Decision and Control, CDC 2019
Countryフランス
CityNice
Period12/11/1912/13/19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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