2025

最新文章

• 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...
• Slowly Varying Regression Under Sparsity AI翻译
  作者：Bertsimas, Dimitris; Digalakis Jr, Vassilis; Li, Michael Lingzhi; Lami, Omar Skali
  作者单位：Massachusetts Institute of Technology (MIT); Hautes Etudes Commerciales (HEC) Paris; Harvard University; McKinsey & Company
  摘要：We introduce the framework of slowly varying regression under sparsity, which allows sparse regression models to vary slowly and sparsely. We formulate the problem of parameter estimation as a mixed -integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem through a novel relaxation. The relaxation utilizes a new equality on Moore -Penrose inverses that convexifies the nonconvex objective function while coinciding with the origina...
• An Unexpected Stochastic Dominance: Pareto Distributions, Dependence, and Diversification AI翻译
  作者：Chen, Yuyu; Embrechts, Paul; Wang, Ruodu
  作者单位：University of Melbourne; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Waterloo
  摘要：We find the perhaps surprising inequality that the weighted average of independent and identically distributed Pareto random variables with infinite mean is larger than one such random variable in the sense of first -order stochastic dominance. This result holds for more general models including super-Pareto distributions, negative dependence, and triggering events and yields superadditivity of the risk measure value -at -risk for these models.
• 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...
• Quantile Optimization via Multiple-Timescale Local Search for Black-Box Functions AI翻译
  作者：Hu, Jiaqiao; Song, Meichen; Fu, Michael C.
  作者单位：State University of New York (SUNY) System; Stony Brook University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：We consider quantile optimization of black-box functions that are estimated with noise. We propose two new iterative three-timescale local search algorithms. The first algorithm uses an appropriately modified finite-difference-based gradient estimator that requires 2d + 1 samples of the black-box function per iteration of the algorithm, where d is the number of decision variables (dimension of the input vector). For higher-dimensional problems, this algorithm may not be practical if the black-...
• Designing Service Menus for Bipartite Queueing Systems AI翻译
  作者：Caldentey, Rene; Hillas, Lisa Aoki; Gupta, Varun
  作者单位：University of Chicago; University of Auckland; Northwestern University
  摘要：We consider a multiclass, multiserver queueing system, in which customers of different types have heterogeneous preferences over the many servers available. The goal of the service provider is to design a menu of service classes that balances two competing objectives: (1) maximize customers' average matching reward and (2) minimize customers' average waiting time. A service class corresponds to a single queue served by a subset of servers under a first come, first served-assign longest idle se...
• Technical Note-On the Convergence Rate of Stochastic Approximation for Gradient-Based Stochastic Optimization AI翻译
  作者：Hu, Jiaqiao; Fu, Michael C.
  作者单位：State University of New York (SUNY) System; Stony Brook University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：We consider stochastic optimization via gradient -based search. Under a stochastic approximation framework, we apply a recently developed convergence rate analysis to provide a new finite -time error bound for a class of problems with convex differentiable structures. For noisy black -box functions, our main result allows us to derive finite -time bounds in the setting where the gradients are estimated via finite -difference estimators, including those based on randomized directions such as th...
• Optimal Diagonal Preconditioning AI翻译
  作者：Qu, Zhaonan; Gao, Wenzhi; Hinder, Oliver; Ye, Yinyu; Zhou, Zhengyuan
  作者单位：Stanford University; Stanford University; Stanford University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; New York University
  摘要：Preconditioning has long been a staple technique in optimization, often applied to reduce the condition number of a matrix and speed up the convergence of algorithms. Although there are many popular preconditioning techniques in practice, most lack guarantees on reductions in condition number, and the degree to which we can improve over existing heuristic preconditioners remains an important question. In this paper, we study the problem of optimal diagonal preconditioning that achieves maximal...
• Robust CARA Optimization AI翻译
  作者：Chen, Li; Sim, Melvyn
  作者单位：University of Sydney; National University of Singapore
  摘要：We propose robust optimization models and their tractable approximations that cater for ambiguity -averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst -case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent factors with ambiguous marginals. To obtain computationally tractable formulations, we propose a hierarchy of app...
• 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; Columbia University; NewYork-Presbyterian Hospital; 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...