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 1295-1315 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。在另一篇论文 [C. Buchheim, A. GrüglerBuchheim、A. Grütering 和 C. Meyer,SIAM J. Optim.,arXiv:2203.07121,2024]中,我们将可行切换模式凸壳的[数学]封闭描述为由有限维投影得出的凸集的交集。在本文中,所得到的外部描述被用于构造函数空间中的外部逼近算法,其迭代在[math]中被证明强烈收敛于凸化最优控制问题的全局最小值。外近似算法的每次迭代中出现的线性二次子问题都是通过半滑牛顿法求解的。在两个空间维度上的一个数值示例说明了整个算法的效率。
Parabolic Optimal Control Problems with Combinatorial Switching Constraints, Part II: Outer Approximation Algorithm
SIAM Journal on Optimization, Volume 34, Issue 2, Page 1295-1315, 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. In a companion paper [C. Buchheim, A. Grütering, and C. Meyer, SIAM J. Optim., arXiv:2203.07121, 2024], we describe the [math]-closure of the convex hull of feasible switching patterns as the intersection of convex sets derived from finite-dimensional projections. In this paper, the resulting outer description is used for the construction of an outer approximation algorithm in function space, whose iterates are proven to converge strongly in [math] to the global minimizer of the convexified optimal control problem. The linear-quadratic subproblems arising in each iteration of the outer approximation algorithm are solved by means of a semismooth Newton method. A numerical example in two spatial dimensions illustrates the efficiency of the overall algorithm.
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.