5期

最新文章

• Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms AI翻译
  作者：Scroccaro, Pedro Zattoni; Atasoy, Bilge; Esfahani, Peyman Mohajerin
  作者单位：Delft University of Technology; Delft University of Technology
  摘要：In inverse optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal. From a learning perspective, the goal is to learn the expert's cost function given a data set of signals and corresponding optimal actions. Motivated by the geometry of the IO set of consistent cost vectors, we introduce the incenter concept, a new notion akin to the recently proposed circumcenter concept. Discussing the geometric and robustness interpretation of the incenter cost ve...
• Change-Point Detection in Dynamic Networks with Missing Links AI翻译
  作者：Enikeeva, Farida; Klopp, Olga
  作者单位：Universite de Poitiers
  摘要：Structural changes occur in dynamic networks quite frequently and their detection is an important question in many situations, such as fraud detection or cybersecurity. Real-life networks are often incompletely observed because of individual nonresponse or network size. In the present paper, we consider the problem of change-point detection at a temporal sequence of partially observed networks. The goal is to test whether there is a change in the network parameters. Our approach is based on th...
• Convolution Bounds on Quantile Aggregation AI翻译
  作者：Blanchet, Jose; Lam, Henry; Liu, Yang; Wang, Ruodu
  作者单位：Stanford University; Columbia University; The Chinese University of Hong Kong, Shenzhen; University of Waterloo
  摘要：Quantile aggregation with dependence uncertainty has a long history in probability theory, with wide applications in finance, risk management, statistics, and operations research. Using a recent result on inf-convolution of quantile-based risk measures, we establish new analytical bounds for quantile aggregation, which we call convolution bounds. Convolution bounds both unify every analytical result available in quantile aggregation and enlighten our understanding of these methods. These bound...
• Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches AI翻译
  作者：Simchi-Levi, David; Xu, Yunzong; Zhao, Jinglong
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Boston University
  摘要：This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constrai...
• A Robust Optimization Approach to Network Control Using Local Information Exchange AI翻译
  作者：Darivianakis, Georgios; Georghiou, Angelos; Shafiee, Soroosh; Lygeros, John
  作者单位：University of Cyprus; Cornell University; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the network. Such policy designs with centralized information exchange result in optimization problems that are typically hard to solve, require establishing substantial communication links, and do not promote privacy since all information is shared among the agents. Designing policies based on arbitrary communication s...
• Ranking and Contextual Selection AI翻译
  作者：Keslin, Gregory; Nelson, Barry L.; Pagnoncelli, Bernardo; Plumlee, Matthew; Rahimian, Hamed
  作者单位：Northwestern University; Universite Cote d'Azur; SKEMA Business School; Amazon.com; Clemson University
  摘要：This paper proposes a new ranking-and-selection procedure, called ranking and contextual selection, in which covariates provide context for data-driven decisions. Our procedure optimizes over a set of covariate design points off-line and then, given an actual observation of the covariate, makes an online decision based on classification-a distinctly new approach. We prove the existence of an experimental design that yields a pointwise probability of good selection guarantee and derive a postex...
• D-Optimal Orienteering for Post-Earthquake Reconnaissance Planning AI翻译
  作者：Wang, Jiaqi; Xie, Weijun; Ryzhov, Ilya O.; Markovic, Nikola; Ou, Ge
  作者单位：University System of Maryland; University of Maryland College Park; University System of Georgia; Georgia Institute of Technology; University System of Maryland; University of Maryland College Park; Utah System of Higher Education; University of Utah; State University System of Florida; University of Florida
  摘要：Immediately following a major earthquake, reconnaissance surveys seek to assess structural damage throughout the region with the help of a limited number of on-ground inspections. The goal is to collect informative and representative data that will guide subsequent relief efforts. We formulate a new type of vehicle routing problem, in which vehicles are tasked with data collection, and the objective function measures data quality using a nonlinear, nonseparable experimental design criterion. W...
• Multi-item Online Order Fulfillment in a Two-Layer Network AI翻译
  作者：Zhao, Yanyang; Wang, Xinshang; Xin, Linwei
  作者单位：University of Chicago; Alibaba Group
  摘要：The global e-commerce boom has driven rapid expansion of fulfillment infrastructure, with e-retailers building more warehouses to offer faster deliveries. However, fulfillment costs have surged over the past decade. This paper addresses the problem of minimizing these costs, where an e-retailer must decide in real time which warehouse(s) will fulfill each order, considering inventory constraints. Orders can be split among warehouses at an additional cost. We focus on a regional distribution ce...
• Better Regularization for Sequential Decision Spaces: Fast Convergence Rates for Nash, Correlated, and Team Equilibria AI翻译
  作者：Farina, Gabriele; Kroer, Christian; Sandholm, Tuomas
  作者单位：Columbia University; Carnegie Mellon University
  摘要：We study the application of iterative first-order methods to the problem of computing equilibria of large-scale extensive-form games. First-order methods must typically be instantiated with a regularizer that serves as a distance-generating function (DGF) for the decision sets of the players. In this paper, we introduce a new weighted entropy-based distance-generating function. We show that this function is equivalent to a particular set of new weights for the dilated entropy distance-generati...
• Near-Optimal Mixed (s, S) Policy for a Multiwarehouse, Multistore Inventory System with Lost Sales and Fixed Cost AI翻译
  作者：Miao, Sentao; Jasin, Stefanus; Chao, Xiuli
  作者单位：University of Colorado System; University of Colorado Boulder; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：We consider a firm managing a multiperiod, multiwarehouse, multistore (MWMS) inventory problem with fixed ordering cost at each store over a finite time horizon. The warehouses are endowed with initial inventories at the start of the horizon, and the stores are periodically replenished from the warehouses. The decisions are the order quantities from each store at each period. The optimal policy for this problem is complex and computationally intractable. We construct a mixed (s, S) policy base...