Singular perturbations in stochastic optimal control with unbounded data
IF 1.3
3区 数学
Q4 AUTOMATION & CONTROL SYSTEMS
Esaim-Control Optimisation and Calculus of Variations
Pub Date : 2022-08-01
DOI:10.1051/cocv/2023020
引用次数: 3
Abstract
Abstract. We study singular perturbations of a class of two-scale stochastic control systems with unbounded data. The assumptions are designed to cover some relaxation problems for deep neural networks. We construct effective Hamiltonian and initial data and prove the convergence of the value function to the solution of a limit (effective) Cauchy problem for a parabolic equation of HJB type. We use methods of probability, viscosity solutions and homogenization.无界数据随机最优控制中的奇异扰动
摘要研究了一类具有无界数据的双尺度随机控制系统的奇异摄动。这些假设旨在涵盖深度神经网络的一些松弛问题。构造了HJB型抛物方程的有效哈密顿量和初始数据，并证明了值函数对极限(有效)Cauchy问题解的收敛性。我们使用概率法、粘度解法和均质法。
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来源期刊
Esaim-Control Optimisation and Calculus of Variations
Mathematics-Computational Mathematics
自引率
7.10%
发文量
77
期刊介绍：
ESAIM: COCV strives to publish rapidly and efficiently papers and surveys in the areas of Control, Optimisation and Calculus of Variations.
Articles may be theoretical, computational, or both, and they will cover contemporary subjects with impact in forefront technology, biosciences, materials science, computer vision, continuum physics, decision sciences and other allied disciplines.
Targeted topics include:
in control: modeling, controllability, optimal control, stabilization, control design, hybrid control, robustness analysis, numerical and computational methods for control, stochastic or deterministic, continuous or discrete control systems, finite-dimensional or infinite-dimensional control systems, geometric control, quantum control, game theory;
in optimisation: mathematical programming, large scale systems, stochastic optimisation, combinatorial optimisation, shape optimisation, convex or nonsmooth optimisation, inverse problems, interior point methods, duality methods, numerical methods, convergence and complexity, global optimisation, optimisation and dynamical systems, optimal transport, machine learning, image or signal analysis;
in calculus of variations: variational methods for differential equations and Hamiltonian systems, variational inequalities; semicontinuity and convergence, existence and regularity of minimizers and critical points of functionals, relaxation; geometric problems and the use and development of geometric measure theory tools; problems involving randomness; viscosity solutions; numerical methods; homogenization, multiscale and singular perturbation problems.
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