pySLEQP: A Sequential Linear Quadratic Programming Method Implemented in Python

Felix Lenders (), Christian Kirches () and Hans Georg Bock ()
Additional contact information
Felix Lenders: Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR)
Christian Kirches: Technische Universität Carolo-Wilhelmina zu Braunschweig, Institut für Mathematische Optimierung
Hans Georg Bock: Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR)

A chapter in Modeling, Simulation and Optimization of Complex Processes HPSC 2015, 2017, pp 103-113 from Springer

Abstract: Abstract We present a prototype implementation of a Sequential Linear Equality-Constrained Qudratic Programming (SLEQP) method for solving the nonlinear programming problem. Similar to SQP active set methods, SLEQP methods are iterative Newton-type methods. In every iteration, a trust region constrained linear programming problem is solved to estimate the active set. Subsequently, a trust region equality constrained quadratic programming problem is solved to obtain a step that promotes locally superlinear convergence. This class of methods has several appealing properties for future research in large-scale nonlinear programming. Implementations of SLEQP methods accessible for research, however, are scarcely found. To this end, we present pySLEQP, an implementation of an SLEQP method in Python. The performance and robustness of the method and our implementation are assessed using the CUTEst and CUTEr benchmark collections of nonlinear programming problems. pySLEQP is found to show robust behavior and reasonable performance.

Keywords: Est Cut; Trust Region; Nonlinear Programming Problem; Newton-type Step; NumPy Array (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-67168-0_9

Ordering information: This item can be ordered from
http://www.springer.com/9783319671680

DOI: 10.1007/978-3-319-67168-0_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().