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作者：Muhle-Karbe, Johannes; Oomen, Roel
作者单位：Imperial College London; Deutsche Bank
摘要：This paper studies a dealer that pre-hedges an anticipated potential trade, and we analyze how this affects the client's overall execution outcome. We show that prehedging can benefit both parties: Improved risk management over an extended horizon enables the dealer to charge reduced spreads that more than offset any adverse impact the pre-hedging activity has on the execution price. However, when a dealer pre-hedges too aggressively, this can be detrimental to the client. Timing uncertainty o...
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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 (...
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作者：Hou, Di; Tang, Tianyun; Toh, Kim-Chuan
作者单位：National University of Singapore; National University of Singapore
摘要：Doubly nonnegative (DNN) programming problems are challenging to solve because of their huge number of ohm(n2) constraints and ohm(n2) variables. In this work, introduce RiNNAL, a method for solving DNN relaxations of large-scale mixed-binary quadratic programs by leveraging their solutions' possible low-rank property. RiNNAL a globally convergent Riemannian augmented Lagrangian method (ALM) that penalizes the nonnegativity and complementarity constraints while preserving all other constraints...
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作者：Bobbio, Federico; Carvalho, Margarida; Lodi, Andrea; Ricos, Ignacio; Torrico, Alfredo
作者单位：Universite de Montreal; Universite de Montreal; Technion Israel Institute of Technology; University of Texas System; University of Texas Dallas; Cornell University
摘要：Motivated by the shortage of seats that the Chilean school choice system is facing, we introduce the problem of jointly increasing school capacities and finding a studentoptimal assignment in the expanded market. Because of the theoretical and practical complexity of the problem, we provide a comprehensive set of tools to solve the problem, including different mathematical programming formulations, a cutting-plane algorithm, and two heuristics that allow obtaining near-optimal solutions quickl...
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作者：Balseiro, Santiago R.; Ma, Will; Zhang, Wenxin
作者单位：Columbia University
摘要：Motivated by real-world applications, such as rental and cloud computing services, we investigate pricing for reusable resources. We consider a system where a single resource with a fixed number of identical copies serves customers with heterogeneous willingness to pay (WTP), and the usage duration distribution is general. Optimal dynamic policies are computationally intractable when usage durations are not memoryless, so the existing literature has focused on static pricing, which incurs a st...
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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...
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作者：Bu, Like; Dawande, Milind; Janakiraman, Ganesh
作者单位：University of Texas System; University of Texas Dallas
摘要：We study effective mechanisms for a manufacturer (buyer) to procure components of an assembly system under asymmetric information (including private costs and unobservable effort) and supply uncertainty. For each component, the buyer has access to an unreliable supplier whose production cost is private, input effort is unobservable, and production yield is uncertain. Further, both the buyer and the supplier have access to a more expensive but reliable supply source. The supplier also has acces...
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作者：Chen, Ningyuan; Li, Anran; Yang, Shuoguang
作者单位：University of Toronto; Chinese University of Hong Kong; Hong Kong University of Science & Technology
摘要：We consider the revenue maximization problem for an online retailer who plans to display in order a set of products differing in their prices and qualities. Consumers have attention spans, that is, the maximum number of products they are willing to view, and inspect the products sequentially before purchasing a product or leaving the platform empty-handed when the attention span gets exhausted. Our framework extends the wellknown cascade model in two directions: random attention spans of a rep...
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作者：Bichuch, Maxim; Feinstein, Zachary
作者单位：State University of New York (SUNY) System; University at Albany, SUNY; University at Buffalo, SUNY; Stevens Institute of Technology
摘要：Within this work we consider an axiomatic framework for Automated Market Makers (AMMs). AMMs are smart contracts that set prices for swaps on a pool of assets. By imposing reasonable axioms on the underlying utility function, we are able to characterize the properties of the swap size of the assets and of the resulting pricing oracle. In providing these general axioms, we define a novel measure of price impacts that can be used to quantify those costs between different AMM constructions. We ha...
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作者：Shapiro, Alexander; Zhou, Enlu; Lin, Yifan; Wang, Yuhao
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：Stochastic optimal control with unknown randomness distributions has been studied for a long time, encompassing robust control, distributionally robust control, and adaptive control. We propose a new episodic Bayesian approach that incorporates Bayesian learning with optimal control. In each episode, the approach learns the randomness distribution with a Bayesian posterior and subsequently solves the corresponding Bayesian average estimate of the true problem. The resulting policy is exercised...
