2025

最新文章

• Fluid Policies, Reoptimization, and Performance Guarantees in Dynamic Resource Allocation AI翻译
  作者：Brown, David B.; Zhang, Jingwei
  作者单位：Duke University; The Chinese University of Hong Kong, Shenzhen
  摘要：Many sequential decision problems involve deciding how to allocate shared resources across a set of independent systems at each point in time. A classic example is the restless bandit problem, in which a budget constraint limits the selection of arms. Fluid relaxations provide a natural approximation technique for this broad class of problems. A recent stream of research has established strong performance guarantees for feasible policies based on fluid relaxations. In this paper, we generalize...
• Mixed-Integer Optimization with Constraint Learning AI翻译
  作者：Maragno, Donato; Wiberg, Holly; Bertsimas, Dimitris; Birbil, S. . Ilker; den Hertog, Dick; Fajemisin, Adejuyigbe O.
  作者单位：University of Amsterdam; Carnegie Mellon University; Massachusetts Institute of Technology (MIT)
  摘要：We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which constraints and objectives are directly learned from data using machine learning, and the trained models are embedded in an optimization formulation. We exploit the mixed-integer optimization representability of many machine learning methods, including linear models, decision trees, ensembles, and multilayer perceptro...
• Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience AI翻译
  作者：Brubach, Brian; Grammel, Nathaniel; Ma, Will; Srinivasan, Aravind
  作者单位：Wellesley College; University System of Maryland; University of Maryland College Park; Columbia University; Columbia University
  摘要：We study generalizations of online bipartite matching in which each arriving vertex (customer) views a ranked list of offline vertices (products) and matches to (purchases) the first one they deem acceptable. The number of products that the customer has patience to view can be stochastic and dependent on the products seen. We develop a framework that views the interaction with each customer as an abstract resource consumption process and derive new results for these online matching problems un...
• Improved Sample-Complexity Bounds in Stochastic Optimization AI翻译
  作者：Baveja, Alok; Chavan, Amit; Nikiforov, Andrei; Srinivasan, Aravind; Xu, Pan
  作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University Camden; Rutgers University New Brunswick; University System of Maryland; University of Maryland College Park; New Jersey Institute of Technology
  摘要：Real-world network-optimization problems often involve uncertain parameters during the optimization phase. Stochastic optimization is a key approach introduced in the 1950s to address such uncertainty. This paper presents improved upper bounds on the number of samples required for the sample-average approximation method in stochastic optimization. It enhances the sample complexity of existing approaches in this setting, providing faster approximation algorithms for any method that employs this...
• Distributionally Robust Optimization Under Distorted Expectations AI翻译
  作者：Cai, Jun; Li, Jonathan Yu-Meng; Mao, Tiantian
  作者单位：University of Waterloo; University of Ottawa; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Distributionally robust optimization (DRO) has arisen as an important paradigm for addressing the issue of distributional ambiguity in decision optimization. In the case in which a decision maker is not risk neutral, the most common scheme applied in DRO for capturing the risk attitude is to employ an expected utility functional. In this paper, we propose to address a decision maker's risk attitude in DRO by following an alternative scheme known as dual expected utility. In this scheme, a dist...
• Customer Scheduling in Large Service Systems Under Model Uncertainty AI翻译
  作者：Chai, Shiwei; Sun, Xu; Abouee-Mehrizi, Hossein
  作者单位：State University System of Florida; University of Florida; University of Miami; University of Waterloo
  摘要：Scheduling in the context of many-server queues has received considerable attention. When there are multiple customer classes and many servers, it is common to make simplifying assumptions that result in a low-fidelity model, potentially leading to model misspecification. However, empirical evidence suggests that these assumptions may not accurately reflect real-world scenarios. Although relaxing these assumptions can yield a more accurate high-fidelity model, it often becomes complex and chal...
• A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation AI翻译
  作者：Zheng, Yufeng; Zheng, Zeyu; Zhu, Tingyu
  作者单位：University of Toronto; University of California System; University of California Berkeley
  摘要：We propose a framework that integrates classic Monte Carlo simulators and Wasserstein generative adversarial networks to model, estimate, and simulate a broad class of arrival processes with general nonstationary and multidimensional random arrival rates. Classic Monte Carlo simulators have advantages in capturing the interpretable physics of a stochastic object, whereas neural network-based simulators have advantages in capturing less interpretable complicated dependence within a high-dimensi...
• Stochastic Localization Methods for Convex Discrete Optimization via Simulation AI翻译
  作者：Zhang, Haixiang; Zheng, Zeyu; Lavaei, Javad
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：We develop and analyze a set of new sequential simulation-optimization algorithms for large-scale multidimensional discrete optimization via simulation problems with a convexity structure. The large-scale notion refers to that the discrete decision variable has a large number of values from which to choose on each dimension of the decision variable. The proposed algorithms are targeted to identify a solution that is close to the optimal solution given any precision level with any given probabi...
• Learning and Optimization with Seasonal Patterns AI翻译
  作者：Chen, Ningyuan; Wang, Chun; Wang, Longlin
  作者单位：University of Toronto; University Toronto Mississauga; University of Toronto; Tsinghua University; Harvard University
  摘要：A standard assumption adopted in the multiarmed bandit (MAB) framework is that the mean rewards are constant over time. This assumption can be restrictive in the business world as decision makers often face an evolving environment in which the mean rewards are time-varying. In this paper, we consider a nonstationary MAB model with K arms whose mean rewards vary over time in a periodic manner. The unknown periods can be different across arms and scale with the length of the horizon T polynomial...
• Multivariate Almost Stochastic Dominance: Transfer Characterizations and Sufficient Conditions Under Dependence Uncertainty AI翻译
  作者：Mueller, Alfred; Scarsini, Marco; Tsetlin, Ilia; Winkler, Robert L.
  作者单位：Universitat Siegen; Luiss Guido Carli University; INSEAD Business School; Duke University
  摘要：Most often, important decisions involve several unknown attributes. This produces a double challenge in the sense that both assessing the individual multiattribute preferences and assessing the joint distribution of the attributes can be extremely hard. To handle the first challenge, we suggest multivariate almost stochastic dominance, a relation based on bounding marginal utilities. We provide necessary and sufficient characterizations in terms of simple transfers, which are easily communicat...