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...
• The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model AI翻译
  作者：Shi, Laixi; Li, Gen; Wei, Yuting; Chen, Yuxin; Geist, Matthieu; Chi, Yuejie
  作者单位：Johns Hopkins University; Chinese University of Hong Kong; University of Pennsylvania; Yale University
  摘要：This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision processes (RMDPs), aimed at learning a policy that optimizes the worst-case performance when the deployed environment falls within a prescribed uncertainty set around the nominal Markov decision process (MDP). Despite recent efforts, the sample complexity of RMDPs remained mostly unsettled regardless of the uncertai...
• Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives AI翻译
  作者：Behdin, Kayhan; Chen, Wenyu; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, which is a key problem in statistical machine learning. Given n samples from a multivariate Gaussian distribution with p variables, the goal is to estimate the p x p inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an & euro;0-penalized version of the pseudo-likelihood function; most earli...
• On the Complexity of Finding Locally Optimal Solutions in Bilevel Linear Optimization AI翻译
  作者：Prokopyev, Oleg A.; Ralphs, Ted K.
  作者单位：University of Zurich; Lehigh University
  摘要：We consider the computational complexity of finding locally optimal solutions to bilevel linear optimization problems (BLPs), from the leader's perspective. We show that, for any constant c > 0, the problem of finding a leader's solution that is within Euclidean distance c(n) of any locally optimal leader's solution, where n is the total number of variables, is NP-hard. Our derivations exploit techniques similar to those used for the analogous result for quadratic optimization problems (QPs). ...
• Integrated Conditional Estimation-Optimization AI翻译
  作者：Qi, Meng; Grigas, Paul; Shen, Zuo-Jun (max)
  作者单位：Cornell University; University of California System; University of California Berkeley; University of Hong Kong; University of Hong Kong
  摘要：Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the stru...
• A Hierarchical Approach to Robust Stability of Multiclass Queueing Networks AI翻译
  作者：Zhao, Feiyang; Gurvich, Itai; Hasenbein, John J.
  作者单位：University of Texas System; University of Texas Austin; Northwestern University
  摘要：We revisit the global-relative to control policies-stability of multiclass queueing networks. In these, as is known, it is generally insufficient that the nominal utilization at each server is below 100%. Certain policies, although work conserving, may destabilize a network that satisfies the nominal-load conditions; additional conditions on the primitives are needed for global stability (stability under any work-conserving policy). The global-stability region was fully characterized for two-s...