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作者：Gao, Rui; Chen, Xi; Kleywegtc, Anton J.
作者单位：University of Texas System; University of Texas Austin; New York University; University System of Georgia; Georgia Institute of Technology
摘要：Wasserstein distributionally robust optimization (DRO) is an approach to optimization under uncertainty in which the decision maker hedges against a set of probability distributions, specified by a Wasserstein ball, for the uncertain parameters. This approach facilitates robust machine learning, resulting in models that sustain good performance when the data are to some extent different from the training data. This robustness is related to the well-studied effect of regularization. The connect...
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作者：Gupta, Vishal; Huang, Michael; Rusmevichientong, Paat
作者单位：University of Southern California
摘要：Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoid...
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作者：Jang, Hyun Jin; Xu, Zuo Quan; Zheng, Harry
作者单位：Ulsan National Institute of Science & Technology (UNIST); Hong Kong Polytechnic University; Imperial College London
摘要：This paper studies an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The utility for luxury goods is not necessarily a concave function. The optimal heterogeneous consumption strategies for a class of nonhomothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all...
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作者：Ghuge, Rohan; Gupta, Anupam; Nagarajan, Viswanath
作者单位：University of Michigan System; University of Michigan; Carnegie Mellon University
摘要：In the stochastic submodular cover problem, the goal is to select a subset of stochastic items of minimum expected cost to cover a submodular function. Solutions in this setting correspond to sequential decision processes that select items one by one adaptively (depending on prior observations). Whereas such adaptive solutions achieve the best objective, the inherently sequential nature makes them undesirable in many applications. We show how to obtain solutions that approximate fully adaptive...
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作者：Wu, Di; Wang, Yuhao; Zhou, Enlu
作者单位：Amazon.com; University System of Georgia; Georgia Institute of Technology
摘要：We consider a simulation-based ranking and selection (R&S) problem with input uncertainty, in which unknown input distributions can be estimated using input data arriving in batches of varying sizes over time. Each time a batch arrives, additional simulations can be run using updated input distribution estimates. The goal is to confi- dently identify the best design after collecting as few batches as possible. We first introduce a moving average estimator for aggregating simulation outputs gen...
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作者：Nannicini, Giacomo
作者单位：International Business Machines (IBM); IBM USA
摘要：We propose quantum subroutines for the simplex method that avoid classical computation of the basis inverse. We show how to quantize all steps of the simplex algorithm, including checking optimality, unboundedness, and identifying a pivot (i.e., pricing the columns and performing the ratio test) according to Dantzig's rule or the steepest edge rule. The quantized subroutines obtain a polynomial speedup in the dimension of the problem but have worse dependence on other numerical parameters. For...
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作者：Lei, Murray; Liu, Sheng; Jasin, Stefanus; Vakhutinsky, Andrew
作者单位：Queens University - Canada; University of Toronto; University of Michigan System; University of Michigan; Oracle
摘要：We consider a joint inventory and pricing problem with one warehouse and multiple stores in which the retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. Demand at each store follows a Poisson distribution, and unmet demand is immediately lost. The retailer incurs the usual variable ordering, inventory holding, and ...
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作者：Daryalal, Maryam; Bodur, Merve; Luedtke, James R.
作者单位：Universite de Montreal; HEC Montreal; University of Toronto; University of Wisconsin System; University of Wisconsin Madison
摘要：Multistage stochastic programs can be approximated by restricting policies to follow decision rules. Directly applying this idea to problems with integer decisions is difficult because of the need for decision rules that lead to integral decisions. In this work, we introduce Lagrangian dual decision rules (LDDRs) for multistage stochastic mixed-integer programming (MSMIP), which overcome this difficulty by applying decision rules in a Lagrangian dual of the MSMIP. We propose two new bounding t...
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作者：Kim, Youngsoo; Kwon, H. Dharma
作者单位：University of Alabama System; University of Alabama Tuscaloosa; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In the game of investment in the common good, the free rider problem can delay the stakeholders' actions in the form of a mixed strategy equilibrium. However, it has been recently shown that the commonly known form of mixed strategy equilibria of the stochastic war of attrition is destabilized by even the slightest degree of asymmetry between the players. Such extreme instability is contrary to the widely accepted notion that a mixed strategy equilibrium is the hallmark of the war of attrition...
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作者：Wang, Jie; Gao, Rui; Zha, Hongyuan
作者单位：The Chinese University of Hong Kong, Shenzhen; University of Texas System; University of Texas Austin; The Chinese University of Hong Kong, Shenzhen; Shenzhen Institute of Artificial Intelligence & Robotics for Society
摘要：In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from different behavior policy, without execution of the target policy. Reinforcement learning in high-stake environments, such as healthcare and education, is often limited to off-policy settings due to safety or ethical concerns or inability of exploration. Hence, it is imperative to quantify the uncertainty of the off-policy estima...
