A parallel quadratic programming method for dynamic optimization problems

  • Quadratic programming problems (QPs) that arise from dynamic optimization problems typically exhibit a very particular structure. We address the ubiquitous case where these QPs are strictly convex and propose a dual Newton strategy that exploits the block-bandedness similarly to an interior-point method. Still, the proposed method features warmstarting capabilities of active-set methods. We give details for an efficient implementation, including tailored numerical linear algebra, step size computation, parallelization, and infeasibility handling. We prove convergence of the algorithm for the considered problem class. A numerical study based on the open-source implementation qpDUNES shows that the algorithm outperforms both well-established general purpose QP solvers as well as state-of-the-art tailored control QP solvers significantly on the considered benchmark problems.

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Metadaten
Author:Janick V. Frasch, Sebastian Sager, Moritz Diehl
DOI:https://doi.org/10.1007/s12532-015-0081-7
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2015
Tag:Software; Theoretical Computer Science
Volume:7
Issue:3
Page Number:41
First Page:289
Last Page:329
Mathematical Programming Computation :MPC 2015 - Issue 3
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