具有组合切换约束条件的抛物线优化控制问题,第一部分:凸松弛

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED
Christoph Buchheim, Alexandra Grütering, Christian Meyer
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引用次数: 0

摘要

SIAM 优化期刊》,第 34 卷第 2 期,第 1187-1205 页,2024 年 6 月。 摘要。我们考虑的是偏微分方程的最优控制问题,其中控制取值为二进制,但随时间跨度而变化;因此可以将其视为动态开关。切换模式可能受到组合约束,例如切换总数的上限或两次切换之间时间的下限。这种组合约束通常被视为一种额外的复杂因素,在启发式后处理中加以处理,而我们方法的核心是研究所有可行切换模式的凸壳,从而定义控制问题的紧密凸松弛。凸松弛由有限维投影衍生的切割平面建立,可通过多面体组合学进行研究。一个关于有界切换次数的数值示例表明,我们的方法可以显著改善直接连续松弛法给出的对偶约束,而连续松弛法是通过松弛二值性约束得到的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parabolic Optimal Control Problems with Combinatorial Switching Constraints, Part I: Convex Relaxations
SIAM Journal on Optimization, Volume 34, Issue 2, Page 1187-1205, June 2024.
Abstract. We consider optimal control problems for partial differential equations where the controls take binary values but vary over the time horizon; they can thus be seen as dynamic switches. The switching patterns may be subject to combinatorial constraints such as, e.g., an upper bound on the total number of switchings or a lower bound on the time between two switchings. While such combinatorial constraints are often seen as an additional complication that is treated in a heuristic postprocessing, the core of our approach is to investigate the convex hull of all feasible switching patterns in order to define a tight convex relaxation of the control problem. The convex relaxation is built by cutting planes derived from finite-dimensional projections, which can be studied by means of polyhedral combinatorics. A numerical example for the case of a bounded number of switchings shows that our approach can significantly improve the dual bounds given by the straightforward continuous relaxation, which is obtained by relaxing binarity constraints.
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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