The integration of a substantial number of plug-in electric vehicles (PEVs) into power grid scheduling introduces complexities due to stochastic charging and discharging behaviors, which pose significant challenges to...
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Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimen...
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Breadth-first search (BFS) is a fundamental graph algorithm that presents significant challenges for parallel implementation due to irregular memory access patterns, load imbalance and synchronization overhead. In thi...
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Given a set P of n points and a set H of n half-planes in the plane, we consider the problem of computing a smallest subset of points such that each half-plane contains at least one point of the subset. The previously...
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The multimodal optimization problems (MMOPs) require finding multiple optima simultaneously. Evolutionary computation integrated with niching techniques is commonly used to solve MMOPs. However, di...
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This paper presents a comprehensive approach to federated learning in wireless networks. We discuss communication strategies that address packet loss and bitrate limitations in both uplink and downlink transmissions, ...
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Tackling optimization in mixed domains (continuous and discrete decision variables) has recently gained attention, causing the development of various extensions of continuous optimization algorithms. In order to more ...
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We study the ergodic optimization problem over a real analytic expanding circle map. We show that in both the topological and the measure-theoretical senses, a typical Cr performance function has a unique maximizing m...
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We consider a large-scale convex program with functional constraints, where interior point methods are intractable due to the problem size. The effective solution techniques for these problems permit only simple opera...
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We consider a large-scale convex program with functional constraints, where interior point methods are intractable due to the problem size. The effective solution techniques for these problems permit only simple operations at each iteration, and thus are based on primal-dual first-order methods such as the Arrow-Hurwicz-Uzawa subgradient method, which utilize only gradient computations and projections at each iteration. Such primal-dual algorithms admit the interpretation of solving the associated saddle point problem arising from the Lagrange dual. We revisit these methods through the lens of regret minimization from online learning and present a flexible framework. While it is well known that two regret-minimizing algorithms can be used to solve a convex-concave saddle point problem at the standard rate of O (1/root T), our framework for primal-dual algorithms allows us to exploit structural properties such as smoothness and/or strong convexity and achieve better convergence rates in favorable cases. In particular, for non-smooth problems with strongly convex objectives, our primal-dual framework equipped with an appropriate modification of Nesterov's dual averaging algorithm achieves O (1/T) convergence rate.
作者：
Henke, DorotheeLefebvre, HenriSchmidt, MartinThürauf, JohannesUniversity of Passau
School of Business Economics and Information Systems Chair of Business Decisions and Data Science Dr.-Hans-Kapfinger-Str. 30 Passau94032 Germany Trier University
Department of Mathematics Universitätsring 15 Trier54296 Germany
Department Liberal Arts and Social Sciences Discrete Optimization Lab Dr.-Luise-Herzberg-Str. 4 Nuremberg90461 Germany
The literature on pessimistic bilevel optimization with coupling constraints is rather scarce and it has been common sense that these problems are harder to tackle than pessimistic bilevel problems without coupling co...
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