6期

最新文章

• Information Retrieval Under Network Uncertainty: Robust Internet Ranking AI翻译
  作者：Timonina-Farkas, Anna; Seifert, Ralf W.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; International Institute for Management Development (IMD)
  摘要：Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank, etc.). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites in search engine results. Recent works have studied ro...
• On Finite Adaptability in Two-Stage Distributionally Robust Optimization AI翻译
  作者：Han, Eojin; Bandi, Chaithanya; Nohadani, Omid
  作者单位：Southern Methodist University; National University of Singapore
  摘要：In many real applications, practitioners prefer policies that are interpretable and easy to implement. This tendency is magnified in sequential decision-making settings. In this paper, we leverage the concept of finite adaptability to construct policies for two-stage optimization problems. More specifically, we focus on the general setting of distributional uncertainties affecting the right-hand sides of constraints, because in a broad range of applications, uncertainties do not affect the obj...
• On the Structure of Decision Diagram-Representable Mixed-Integer Programs with Application to Unit Commitment AI翻译
  作者：Salemi, Hosseinali; Davarnia, Danial
  作者单位：Iowa State University
  摘要：Over the past decade, decision diagrams (DDs) have been used to model and solve integer programming and combinatorial optimization problems. Despite successful performance of DDs in solving various discrete optimization problems, their extension to model mixed-integer programs (MIPs), such as those appearing in energy applications, is lacking. More broadly, the question of which problem structures admit a DD representation is still open in the DD community. In this paper, we address this quest...
• Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality AI翻译
  作者：Gao, Rui
  作者单位：University of Texas System; University of Texas Austin
  摘要：Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance. Despite its recent empirical success in operations research and machine learning, existing performance guarantees for generic loss functions are either overly conservative because of the curse of dimensionality or plausible only in large sample asymptotics. In this paper, we develop a nonasymptotic framework for analyzing the out-...
• The Anchor-Robust Project Scheduling Problem AI翻译
  作者：Bendotti, Pascale; Chretienne, Philippe; Fouilhoux, Pierre; Pass-Lanneau, Adele
  作者单位：Electricite de France (EDF); Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS)
  摘要：In project scheduling with uncertain processing times, the decision maker often needs to compute a baseline schedule in advance while guaranteeing that some jobs will not be rescheduled later. Standard robust approaches either produce a schedule with a very large makespan or offer no guarantee on starting times of the jobs. The concept of anchor-robustness is introduced as a middle ground between these approaches. A subset of jobs is said to be anchored if the starting times of its jobs in the...
• Technical Note-Average Cost Optimality in Partially Observable Lost-Sales Inventory Systems AI翻译
  作者：Bai, Xingyu; Chen, Xin; Stolyar, Alexander L.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We consider a partially observable lost-sales inventory system, in which the inventory level is observed only when it reaches zero. We use the vanishing discount factor approach to prove the existence of a stationary optimal policy for the average cost minimization. As our main methodological contribution, we provide a way to verify the key condition of the vanishing discount factor approach???the uniform boundedness of the relative discounted value function. To accomplish that, we construct a...
• On the Strength of Relaxations of Weakly Coupled Stochastic Dynamic Programs AI翻译
  作者：Brown, David B.; Zhang, Jingwei
  作者单位：Duke University
  摘要：Many stochastic dynamic programs (DPs) have a weakly coupled structure in that a set of linking constraints in each period couples an otherwise independent collection of subproblems. Two widely studied approximations of such problems are approximate linear programs (ALPs), which involve optimizing value function approximations that additively separate across subproblems, and Lagrangian relaxations, which involve relaxing the linking constraints. It is well known that both of these approximatio...