3期

最新文章

• Branch-and-Price for Prescriptive Contagion Analytics AI翻译
  作者：Jacquillat, Alexandre; Li, Michael Lingzhi; Rame, Martin; Wang, Kai
  作者单位：Massachusetts Institute of Technology (MIT); Harvard University; Massachusetts Institute of Technology (MIT); Tsinghua University
  摘要：Contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision maker allocates shared resources across multiple segments of a population, each governed by continuous -time contagion dynamics. These problems feature a large-scale mixed -integer nonconvex optimization structure with constraints governed by ordinary differential equations. This paper develops a branch -and -price method...
• An Exact Solution to Wordle AI翻译
  作者：Bertsimas, Dimitris; Paskov, Alex
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：In this paper, we propose and scale a framework based on exact dynamic programming to solve the game of Wordle, which has withstood many attempts to be solved by a variety of methods ranging from reinforcement learning to information theory. First, we derive a mathematical model of the game, present the resultant Bellman equation, and outline a series of optimizations to make this approach tractable. We then outline how to extend the framework to solve variants of the game-such as Wordle Hard ...
• Jointly Modeling and Clustering Tensors in High Dimensions AI翻译
  作者：Cai, Biao; Zhang, Jingfei; Sun, Will Wei
  作者单位：City University of Hong Kong; Emory University; Purdue University System; Purdue University
  摘要：We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. To effectively tackle the high dimensionality of tensor objects, we employ plausible dimension reduction assumptions that exploit the intrinsic structures of tensors, such as low rankness in the mean and separability in the covariance. In estimation, we develop an efficient high-dimensional expectation conditional maximization ...
• Assigning and Scheduling Generalized Malleable Jobs Under Subadditive or Submodular Processing Speeds AI翻译
  作者：Fotakis, Dimitris; Matuschke, Jannik; Papadigenopoulos, Orestis
  作者单位：National Technical University of Athens; KU Leuven; Columbia University
  摘要：Malleable scheduling is a model that captures the possibility of parallelization to expedite the completion of time-critical tasks. A malleable job can be allocated and processed simultaneously on multiple machines, occupying the same time interval on all these machines. We study a general version of this setting, in which the functions determining the joint processing speed of machines for a given job follow different discrete concavity assumptions (subadditivity, fractional subadditivity, su...
• Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem AI翻译
  作者：Tsang, Man Yiu; Shehadeh, Karmel S.; Curtis, Frank E.; Hochman, Beth R.; Brentjens, Tricia E.
  作者单位：Lehigh University; NewYork-Presbyterian Hospital; Columbia University; Columbia University; NewYork-Presbyterian Hospital
  摘要：We propose combined allocation, assignment, sequencing, and scheduling problems under uncertainty involving multiple operation rooms (ORs), anesthesiologists, and surgeries as well as methodologies for solving such problems. Specifically, given sets of ORs, regular anesthesiologists, on -call anesthesiologists, and surgeries, our methodologies solve the following decision -making problems simultaneously: (1) an allocation problem that decides which ORs to open and which on -call anesthesiologi...
• Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback AI翻译
  作者：Jordan, Michael; Lin, Tianyi; Zhou, Zhengyuan
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Columbia University; New York University
  摘要：Online gradient descent (OGD) is well-known to be doubly optimal under strong convexity or monotonicity assumptions: (1) in the single -agent setting, it achieves an optimal regret of O ( log T ) for strongly convex cost functions, and (2) in the multiagent setting of strongly monotone games with each agent employing OGD, we obtain lastiterate convergence of the joint action to a unique Nash equilibrium at an optimal rate of O 1 ( ). . Whereas these finite -time guarantees highlight its merits...
• On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design AI翻译
  作者：Anunrojwong, Jerry; Balseiro, Santiago R.; Besbes, Omar
  作者单位：Columbia University
  摘要：Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: The seller is selling an indivisible item to n buyers such that the buyers' valuations are drawn from a joint distribution that is unknown to both the buyers and the seller, buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially c...
• Column-Randomized Linear Programs: Performance Guarantees and Applications AI翻译
  作者：Akchen, Yi-Chun; Misic, Velibor V.
  作者单位：University of London; University College London; University of California System; University of California Los Angeles
  摘要：We propose a randomized method for solving linear programs with a large number of columns but a relatively small number of constraints. Because enumerating all the columns is usually unrealistic, such linear programs are commonly solved by column generation, which is often still computationally challenging because of the intractability of the subproblem in many applications. Instead of iteratively introducing one column at a time as in column generation, our proposed method involves sampling a...
• Revenue Maximization Under Unknown Private Values with Nonobligatory Inspection AI翻译
  作者：Alaei, Saeed; Makhdoumi, Ali; Malekian, Azarakhsh
  作者单位：Alphabet Inc.; Google Incorporated; Duke University; University of Toronto
  摘要：We consider the problem of selling k units of an item to n unit-demand buyers to maximize revenue, where the buyers' values are independently distributed (not necessarily identical) according to publicly known distributions but unknown to the buyers themselves, with the option of allowing buyers to inspect the item at a cost. This problem can be interpreted as a revenue-maximizing variant of Weitzman's Pandora's problem with a nonobligatory inspection. We first fully characterize the optimal m...
• Trading with Concave Price Impact and Impact Decay-Theory and Evidence AI翻译
  作者：Hey, Natascha; Mastromatteo, Iacopo; Muhle-Karbe, Johannes; Webster, Kevin
  作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Imperial College London
  摘要：We study statistical arbitrage problems accounting for the nonlinear and transient price impact of metaorders observed empirically. We show that simple explicit trading rules can be derived even for general nonparametric alpha and liquidity signals and also discuss extensions to several impact decay timescales. These results are illustrated using a proprietary data set of Capital Fund Management metaorders, which allows us to calibrate the levels, concavity, and decay parameters of the price i...