2期

最新文章

• Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees AI翻译
  作者：Li, Yongchun; Xie, Weijun
  摘要：This paper studies a classic maximum entropy sampling problem (MESP), which aims to select the most informative principal submatrix of a prespecified size from a covariance matrix. By investigating its Lagrangian dual and primal characterization, we derive a novel convex integer program for MESP and show that its continuous relaxation yields a near-optimal solution. The results motivate us to develop a sampling algorithm and derive its approximation bound for MESP, which improves the best know...
• Equilibrium Identification and Selection in Finite Games AI翻译
  作者：Cronert, Tobias; Minner, Stefan
  作者单位：Technical University of Munich; Technical University of Munich
  摘要：Finite games provide a framework to model simultaneous competitive decisions among a finite set of players (competitors), each choosing from a finite set of strategies. Potential applications include decisions on competitive production volumes, over capacity decisions to location selection among competitors. The predominant solution concept for finite games is the identification of a Nash equilibrium. We are interested in larger finite games, which cannot efficiently be represented in normal f...
• Flattening Energy-Consumption Curves by Monthly Constrained Direct Load Control Contracts AI翻译
  作者：Fattahi, Ali; Ghodsi, Saeed; Dasu, Sriram; Ahmad, Reza
  作者单位：Johns Hopkins University; University of California System; University of California Los Angeles; University of Southern California
  摘要：Balancing electricity demand and supply is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system cost. Demand-response programs are among the strategies that utilities use to reduce electricity consumption dur-ing peak hours and flatten the energy-consumption curve. Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign calls to customer groups to reduce their energy usage by ...
• Testing, Voluntary Social Distancing, and the Spread of an Infection AI翻译
  作者：Acemoglu, Daron; Makhdoumi, Ali; Malekian, Azarakhsh; Ozdaglar, Asuman
  作者单位：Massachusetts Institute of Technology (MIT); Duke University; University of Toronto; Massachusetts Institute of Technology (MIT)
  摘要：We study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines a social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. However, greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is nonmonotone. This no...
• Lagrangian Dual Decision Rules for Multistage Stochastic Mixed-Integer Programming AI翻译
  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...
• Robust Queue Inference from Waiting Times AI翻译
  作者：Bandi, Chaithanya; Han, Eojin; Proskynitopoulos, Alexej
  作者单位：National University of Singapore; Southern Methodist University; Northwestern University
  摘要：Observational data from queueing systems are of great practical interest in many application areas because they can be leveraged for better statistical inference of service processes. However, these observations often only provide partial information of the system for various reasons in real-world settings. Moreover, their complex temporal dependence on the queueing dynamics and the absence of distributional information on the model primitives render estimation of queueing systems remarkably c...
• Stochastic Liquidity as a Proxy for Nonlinear Price Impact AI翻译
  作者：Muhle-Karbe, Johannes; Wang, Zexin; Webster, Kevin
  作者单位：Imperial College London
  摘要：Optimal execution and trading algorithms rely on price impact models, such as the propagator model, to quantify trading costs. Empirically, price impact is concave in trade sizes, leading to nonlinear models for which optimization problems are intractable, and even qualitative properties, such as price manipulation, are poorly understood. However, we show that in the diffusion limit of small and frequent orders, the nonlinear model converges to a tractable linear model. In this high-frequency ...
• Black-Box Acceleration of Monotone Convex Program Solvers AI翻译
  作者：London, Palma; Vardi, Shai; Eghbali, Reza; Wierman, Adam
  作者单位：California Institute of Technology; Purdue University System; Purdue University; University of California System; University of California Berkeley
  摘要：This paper presents a black-box framework for accelerating packing optimization solvers. Our method applies to packing linear programming problems and a family of convex programming problems with linear constraints. The framework is designed for high-dimensional problems, for which the number of variables n is much larger than the number of measurements m. Given an (m x n) problem, we construct a smaller (m x epsilon n) problem, whose solution we use to find an approximation to the optimal sol...
• High-Order Steady-State Diffusion Approximations AI翻译
  作者：Braverman, Anton; Dai, J. G.; Fang, Xiao
  作者单位：Northwestern University; Cornell University; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong
  摘要：We derive and analyze new diffusion approximations of stationary distributions of Markov chains that are based on second- and higher-order terms in the expansion of the Markov chain generator. Our approximations achieve a higher degree of accuracy compared with diffusion approximations widely used for the last 50 years while retaining a similar computational complexity. To support our approximations, we present a combination of theoretical and numerical results across three different models. O...
• Data-Driven Ranking and Selection Under Input Uncertainty AI翻译
  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...