An Augmented Lagrangian Method for State Constrained Linear Parabolic Optimal Control Problems

IF 1.6 3区 数学 Q2 MATHEMATICS, APPLIED
Hailing Wang, Changjun Yu, Yongcun Song
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引用次数: 0

Abstract

In this paper, we consider a class of state constrained linear parabolic optimal control problems. Instead of treating the inequality state constraints directly, we reformulate the problem as an equality-constrained optimization problem, and then apply the augmented Lagrangian method (ALM) to solve it. We prove the convergence of the ALM without any existence or regularity assumptions on the corresponding Lagrange multipliers, which is an essential complement to the classical theoretical results for the ALM because restrictive regularity assumptions are usually required to guarantee the existence of the Lagrange multipliers associated with the state constraints. In addition, under an appropriate choice of penalty parameter sequence, we can obtain a super-linear non-ergodic convergence rate for the ALM. Computationally, we apply a semi-smooth Newton (SSN) method to solve the ALM subproblems and design an efficient preconditioned conjugate gradient method for solving the Newton systems. Some numerical results are given to illustrate the effectiveness and efficiency of our algorithm.

Abstract Image

状态受限线性抛物线优化控制问题的增量拉格朗日法
本文考虑了一类状态约束线性抛物线最优控制问题。我们没有直接处理不平等状态约束,而是将问题重新表述为平等约束优化问题,然后应用增强拉格朗日法(ALM)求解。我们证明了 ALM 的收敛性,而无需对相应的拉格朗日乘数做任何存在性或正则性假设,这是对 ALM 经典理论结果的重要补充,因为要保证与状态约束相关的拉格朗日乘数的存在,通常需要限制性的正则性假设。此外,在适当选择惩罚参数序列的情况下,我们可以获得 ALM 的超线性非啮合收敛率。在计算上,我们采用半光滑牛顿(SSN)方法来求解 ALM 子问题,并设计了一种高效的预条件共轭梯度法来求解牛顿系统。我们给出了一些数值结果,以说明我们算法的有效性和效率。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
149
审稿时长
9.9 months
期刊介绍: The Journal of Optimization Theory and Applications is devoted to the publication of carefully selected regular papers, invited papers, survey papers, technical notes, book notices, and forums that cover mathematical optimization techniques and their applications to science and engineering. Typical theoretical areas include linear, nonlinear, mathematical, and dynamic programming. Among the areas of application covered are mathematical economics, mathematical physics and biology, and aerospace, chemical, civil, electrical, and mechanical engineering.
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