2期

最新文章

• Structural Estimation of Markov Decision Processes in High Dimensional State Space with Finite-Time Guarantees AI翻译
  作者：Zeng, Siliang; Hong, Mingyi; Garcia, Alfredo
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Texas A&M University System; Texas A&M University College Station
  摘要：We consider the task of estimating a structural model of dynamic decisions by human agent based on the observable history of implemented actions and visited states. This problem has an inherent nested structure: In the inner problem, an optimal policy a given reward function is identified, whereas in the outer problem, a measure of fit is maximized. Several approaches have been proposed to alleviate the computational burden this nested-loop structure, but these methods still suffer from high c...
• 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...
• 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...
• 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...
• Asymptotically Tight Bounds on the Optimal Pricing Strategy with Patient Customers AI翻译
  作者：Qian, Shuaijie; Su, Xizhi; Zhou, Chao
  作者单位：Hong Kong University of Science & Technology; National University of Singapore; National University of Singapore
  摘要：This work considers a monopolist seller facing both patient and impatient customers. Given the current price, the impatient customers will either purchase or leave immediately, depending on the relative magnitude between this price and their valuation of the product. In comparison, the patient customers will wait for some periods to see if the price will drop to their valuation, and if that occurs, they will purchase immediately. The monopolist designs the pricing strategy to maximize the long...
• Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements AI翻译
  作者：Jalota, Devansh; Ye, Yinyu
  作者单位：Stanford University; Stanford University
  摘要：Fisher markets are one of the most fundamental models for resource allocation. However, the problem of computing equilibrium prices in Fisher markets typically relies on complete knowledge of users' budgets and utility functions and requires transactions to happen in a static market where all users are present simultaneously. Motivated by these practical considerations, we study an online variant of Fisher markets, wherein users with privately known utility and budget parameters, drawn indepen...
• Technical Note-On the Convergence Rate of Stochastic Approximation for Gradient-Based Stochastic Optimization AI翻译
  作者：Hu, Jiaqiao; Fu, Michael C.
  作者单位：State University of New York (SUNY) System; Stony Brook University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：We consider stochastic optimization via gradient -based search. Under a stochastic approximation framework, we apply a recently developed convergence rate analysis to provide a new finite -time error bound for a class of problems with convex differentiable structures. For noisy black -box functions, our main result allows us to derive finite -time bounds in the setting where the gradients are estimated via finite -difference estimators, including those based on randomized directions such as th...
• 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...
• Linear Classifiers Under Infinite Imbalance AI翻译
  作者：Glasserman, Paul; Li, Mike
  作者单位：Columbia University
  摘要：We study the behavior of linear discriminant functions for binary classification in the infinite-imbalance limit, where the sample size of one class grows without bound while the sample size of the other remains fixed. The coefficients of the classifier minimize an empirical loss specified through a weight function. We show that for a broad class of weight functions, the intercept diverges but the rest of the coefficient vector has a finite almost sure limit under infinite imbalance, extending...