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pySLEQP: A Sequential Linear Quadratic Programming Method Implemented in Python

In: Modeling, Simulation and Optimization of Complex Processes HPSC 2015

Author

Listed:
  • 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))

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.

Suggested Citation

  • Felix Lenders & Christian Kirches & Hans Georg Bock, 2017. "pySLEQP: A Sequential Linear Quadratic Programming Method Implemented in Python," Springer Books, in: Hans Georg Bock & Hoang Xuan Phu & Rolf Rannacher & Johannes P. Schlöder (ed.), Modeling, Simulation and Optimization of Complex Processes HPSC 2015, pages 103-113, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-67168-0_9
    DOI: 10.1007/978-3-319-67168-0_9
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