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作者：Chen, Yuyu; Embrechts, Paul; Wang, Ruodu
作者单位：University of Melbourne; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Waterloo
摘要：We find the perhaps surprising inequality that the weighted average of independent and identically distributed Pareto random variables with infinite mean is larger than one such random variable in the sense of first -order stochastic dominance. This result holds for more general models including super-Pareto distributions, negative dependence, and triggering events and yields superadditivity of the risk measure value -at -risk for these models.
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作者：Scully, Ziv; van Kreveld, Lucas
作者单位：Cornell University; Eindhoven University of Technology
摘要：We consider scheduling in the M/G/1 queue with unknown job sizes. It is known that the Gittins policy minimizes mean response time in this setting. However, the behavior of the tail of response time under Gittins is poorly understood, even in the largeresponse -time limit. Characterizing Gittins's asymptotic tail behavior is important because if Gittins has optimal tail asymptotics, then it simultaneously provides optimal mean response time and good tail performance. In this work, we give the ...
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作者：Caldentey, Rene; Hillas, Lisa Aoki; Gupta, Varun
作者单位：University of Chicago; University of Auckland; Northwestern University
摘要：We consider a multiclass, multiserver queueing system, in which customers of different types have heterogeneous preferences over the many servers available. The goal of the service provider is to design a menu of service classes that balances two competing objectives: (1) maximize customers' average matching reward and (2) minimize customers' average waiting time. A service class corresponds to a single queue served by a subset of servers under a first come, first served-assign longest idle se...
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作者：Qu, Zhaonan; Gao, Wenzhi; Hinder, Oliver; Ye, Yinyu; Zhou, Zhengyuan
作者单位：Stanford University; Stanford University; Stanford University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; New York University
摘要：Preconditioning has long been a staple technique in optimization, often applied to reduce the condition number of a matrix and speed up the convergence of algorithms. Although there are many popular preconditioning techniques in practice, most lack guarantees on reductions in condition number, and the degree to which we can improve over existing heuristic preconditioners remains an important question. In this paper, we study the problem of optimal diagonal preconditioning that achieves maximal...
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作者：Yang, Shuoguang; Li, Xudong; Lan, Guanghui
作者单位：Hong Kong University of Science & Technology; Fudan University; University System of Georgia; Georgia Institute of Technology
摘要：Attention to data-driven optimization approaches, including the well-known stochastic gradient descent method, has grown significantly over recent decades, but data driven constraints have rarely been studied because of the computational challenges of projections onto the feasible set defined by these hard constraints. In this paper, we focus on the nonsmooth convex-concave stochastic minimax regime and formulate the data-driven constraints as expectation constraints. The minimax expectation c...
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作者：Yang, Yu
作者单位：State University System of Florida; University of Florida
摘要：In this paper, we propose an innovative variable fixing strategy called deep Lagrangian underestimate fi xing (DeLuxing). It is a highly effective approach for removing unnecessary variables in column-generation (CG)-based exact methods used to solve challenging discrete optimization problems commonly encountered in various industries, including vehicle routing problems (VRPs). DeLuxing employs a novel linear programming (LP) formulation with only a small subset of the enumerated variables, wh...
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作者：Chen, Li; Sim, Melvyn
作者单位：University of Sydney; National University of Singapore
摘要：We propose robust optimization models and their tractable approximations that cater for ambiguity -averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst -case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent factors with ambiguous marginals. To obtain computationally tractable formulations, we propose a hierarchy of app...
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作者：Yang, Shuoguang; Fang, Ethan X.; Shanbhag, Uday V.
作者单位：Hong Kong University of Science & Technology; Duke University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：As systems grow in size, scale, and intricacy, the challenges of misspecification become even more pronounced. In this paper, we focus on parametric misspecification in regimes complicated by risk and nonconvexity. When this misspecification may be resolved via a parallel learning process, we develop data -driven schemes for resolving a broad class of misspecified stochastic compositional optimization problems. Notably, this rather broad class of compositional problems can contend with challen...
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作者：Chao, Xiuli; Jasin, Stefanus; Miao, Sentao
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Colorado System; University of Colorado Boulder
摘要：We consider the inventory control problem of a multiwarehouse, multistore system over a time horizon when the warehouses receive no external replenishment. This problem is prevalent in retail settings, and it is referred to in the work of [Jackson PL (1988) Stock allocation in a two -echelon distribution system or what to do until your ship comes in. Management Sci. 34(7):880-895] as the problem of what to do until your (external) shipment comes in. The warehouses are stocked with initial inve...
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作者：Jiang, Jiashuo; Ma, Will; Zhang, Jiawei
作者单位：Hong Kong University of Science & Technology; Columbia University; Columbia University; New York University
摘要：Prophet inequalities are a useful tool for designing online allocation procedures and comparing their performance to the optimal offline allocation. In the basic setting of k-unit prophet inequalities, a well-known procedure with its celebrated performance guarantee of 1-1 root k+3 has found widespread adoption in mechanism design and general online allocation problems in online advertising, healthcare scheduling, and revenue management. Despite being commonly used to derive approximately opti...
