Operations Research

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• Optimized Dimensionality Reduction for Moment-Based Distributionally Robust Optimization AI翻译
  作者：Jiang, Shiyi; Cheng, Jianqiang; Pan, Kai; Shen, Zuo-Jun Max
  作者单位：Hong Kong Polytechnic University; University of Arizona; University of California System; University of California Berkeley; University of Hong Kong; University of Hong Kong
  摘要：Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems can be reformulated as semidefinite programming (...
• Exponential Concentration in Stochastic Approximation AI翻译
  作者：Law, Kody . T. H.; Walton, Neil; Yang, Shangda
  作者单位：University of Manchester; Durham University
  摘要：We analyze the behavior of stochastic approximation algorithms where iterates, in expectation, progress toward an objective at each step. When progress is proportional to the step size of the algorithm, we prove exponential concentration bounds. These tailbounds contrast asymptotic normality results, which are more frequently associated with stochastic approximation. The methods that we develop rely on a proof of geometric ergodicity. The extends results on the exponential ergodicity of Markov...
• Crosscutting Bayesian Mechanism Design for Blockchain Transaction Fee Allocation AI翻译
  作者：Chen, Xi; Simchi-Levi, David; Zhao, Zishuo; Zhou, Yuan
  作者单位：New York University; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University; Tsinghua University
  摘要：In blockchain systems, the design of transaction fee mechanisms (TFMs) is essential for stability and satisfaction for both miners and users. A recent work has proven the impossibility of collusion-proof mechanisms that achieve both nonzero miner revenue and Dominant Strategy Incentive Compatibility (DSIC) for users. However, a positive miner revenue is important in practice to motivate miners. To address this challenge, we consider a Bayesian game setting and relax the DSIC requirement for us...
• Technical Note: Multistage Robust Mixed-Integer Programming AI翻译
  作者：Postek, Krzysztof; Romeijnders, Ward; Wiesemann, Wolfram
  作者单位：Delft University of Technology; University of Groningen; Imperial College London
  摘要：Multistage robust optimization, in which decisions are taken sequentially as new information becomes available about uncertain problem parameters, is a very versatile yet computationally challenging paradigm for decision making under uncertainty. In this technical note, we propose a new model and solution approach for multistage robust mixed-integer programs, which may contain both continuous and discrete decisions at any time stage. Our model builds upon the finite adaptability scheme develop...
• Joint Bottom-up Method for Probabilistic Forecasting of Hierarchical Time Series AI翻译
  作者：Bertani, Nicollo; Jensen, Shane T.; Satopaa, Ville A.
  作者单位：Universidade Catolica Portuguesa; University of Pennsylvania; INSEAD Business School
  摘要：This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness inclusion of an advertisement in this article, neither constitutes nor implies a guaran...
• Learning in Stackelberg Games with Non-myopic Agents AI翻译
  作者：Haghtalab, Nika; Lykouris, Thodoris; Nietert, Sloan; Wei, Alexander
  作者单位：University of California System; University of California Berkeley; Massachusetts Institute of Technology (MIT); Cornell University
  摘要：We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent without knowing the agent's payoff function. Although learning in Stackelberg games is well understood when the agent is myopic, dealing with non-myopic agents poses additional complications. In particular, non-myopic agents may strategize and select actions that are inferior in the present in order to mislead the principal's learning algorithm and obtain better outcomes in the future. We provi...
• Data-Driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information AI翻译
  作者：Zhang, Yiyang; Liu, Junyi; Zhaoa, Xiaobo
  作者单位：Tsinghua University
  摘要：Focusing on stochastic programming (SP) with covariate information, this paper proposes an empirical risk minimization (ERM) method embedded within a nonconvex piecewise affine decision rule (PADR), which aims to learn the direct mapping from features to optimal decisions. We establish the nonasymptotic consistency result of our PADRbased ERM model for unconstrained problems, which illustrates the role of piece number in balancing the trade-off between the approximation and estimation errors. ...
• Erratum to Online Allocation and Pricing: Constant Regret via Bellman Inequalities AI翻译
  作者：Vera, Alberto; Banerjee, Siddhartha; Gurvich, Itai
  作者单位：Cornell University; Cornell University; Northwestern University
  摘要：Theorem 3 of Vera et al. (2021) states a constant regret result for a menu-pricing problem. This erratum preserves theorem 3 but revises its proof. The revision has implications also for the assortment problem in section 5.5 of the paper.
• Adaptive Learning in Uncertain and Sequential Competition AI翻译
  作者：Li, Shukai; Mehrotra, Sanjay
  作者单位：New York University; NYU Shanghai; Northwestern University
  摘要：We investigate an individual's decision-making problem in a competitive and uncertain environment, where N learners (decision makers) confront unknown objective functions, lack competitor data, and optimize actions over a finite horizon of T epochs. Within a general framework, we explore what conditions ensure good performance of learning policies solely based on individual data. We show that when learner objective functions exhibit a tatonnement stability property and individual data are info...
• Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models AI翻译
  作者：Gong, Xueping; You, Wei; Zhang, Tiheng
  作者单位：Xiamen University; Hong Kong University of Science & Technology
  摘要：We study contextual dynamic pricing, where a decision maker posts personalized prices based on observable contexts and receives binary purchase feedback indicating whether the customer's valuation exceeds the price. Each valuation is modeled as an unknown latent function of the context, corrupted by independent and identically distributed market noise from an unknown distribution. Relying only on Lipschitz continuity of the noise distribution and bounded valuations, we propose a minimax-optima...