A parallel quadratic programming method for dynamic optimization problems

作     者：Frasch, Janick V. Sager, Sebastian Diehl, Moritz 

作者机构：Univ Magdeburg Inst Math Optimizat Magdeburg Germany Univ Freiburg Dept Microsyst Engn Freiburg Germany 

出 版 物：《MATHEMATICAL PROGRAMMING COMPUTATION》 (数学规划计算)

年 卷 期：2015年第7卷第3期

页      面：289-329页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：ERC ST HIGHWIND Agentschap voor Innovatie door Wetenschap en Technologie, IWT EMBOCON, (MC ITN-264735, FP7-SADCO, ICT-248940) Fonds Wetenschappelijk Onderzoek, FWO, (G.0320.08) Fonds Wetenschappelijk Onderzoek, FWO Belgian Federal Science Policy Office, BELSPO, (IUAP P7) Belgian Federal Science Policy Office, BELSPO European Commission, EC, (259166) European Commission, EC 

主　　题：Quadratic programming Dual decomposition Model predictive control Structure exploitation Parallel algorithms 

摘      要：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.