2012

最新文章

• Price of Correlations in Stochastic Optimization AI翻译
  作者：Agrawal, Shipra; Ding, Yichuan; Saberi, Amin; Ye, Yinyu
  作者单位：Stanford University; Stanford University
  摘要：When decisions are made in the presence of high-dimensional stochastic data, handling joint distribution of correlated random variables can present a formidable task, both in terms of sampling and estimation as well as algorithmic complexity. A common heuristic is to estimate only marginal distributions and substitute joint distribution by independent (product) distribution. In this paper, we study possible loss incurred on ignoring correlations through a distributionally robust stochastic pro...
• Asymptotic Optimality of Balanced Routing AI翻译
  作者：Chen, Hong; Ye, Heng-Qing
  作者单位：Shanghai Jiao Tong University; Hong Kong Polytechnic University
  摘要：Consider a system with K parallel servers, each with its own waiting room. Upon arrival, a job is routed to the queue of one of the servers. Finding a routing policy that minimizes the total workload in the system is a known difficult problem in general. Even if the optimal policy is identified, the policy would require the full queue length information at the arrival of each job; for example, the join-the-shortest-queue policy (which is known to be optimal for identical servers with exponenti...
• Consistency of Multidimensional Convex Regression AI翻译
  作者：Lim, Eunji; Glynn, Peter W.
  作者单位：University of Miami; Stanford University
  摘要：Convex regression is concerned with computing the best fit of a convex function to a data set of n observations in which the independent variable is (possibly) multidimensional. Such regression problems arise in operations research, economics, and other disciplines in which imposing a convexity constraint on the regression function is natural. This paper studies a least-squares estimator that is computable as the solution of a quadratic program and establishes that it converges almost surely t...
• Sequential Correlated Equilibria in Stopping Games AI翻译
  作者：Heller, Yuval
  作者单位：University of Oxford; University of Oxford
  摘要：In many situations, such as trade in stock exchanges, agents have many opportunities to act within a short interval of time. The agents in such situations can often coordinate their actions in advance, but coordination during the game consumes too much time. An equilibrium in such situations has to be sequential in order to handle mistakes made by players. In this paper, we present a new solution concept for infinite-horizon dynamic games, which is appropriate for such situations: a sequential...
• A Copulas-Based Approach to Modeling Dependence in Decision Trees AI翻译
  作者：Wang, Tianyang; Dyer, James S.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Texas System; University of Texas Austin
  摘要：This paper presents a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows multiple dependent uncertainties with arbitrary marginal distributions to be represented in a decision tree with a sequence of conditional probability distributions. This general framework could be naturally applied in decision analysis and real options valuations, as well as in more general applications...
• Operations Research: The Next Three Years AI翻译
  作者：Zenios, Stefanos
  作者单位：Stanford University
• Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines AI翻译
  作者：Epstein, Rafael; Goic, Marcel; Weintraub, Andres; Catalan, Jaime; Santibanez, Pablo; Urrutia, Rodolfo; Cancino, Raul; Gaete, Sergio; Aguayo, Augusto; Caro, Felipe
  作者单位：Universidad de Chile; University of California System; University of California Los Angeles
  摘要：We present a methodology for long-term mine planning based on a general capacitated multicommodity network flow formulation. It considers underground and open-pit ore deposits sharing multiple downstream processing plants over a long horizon. The purpose of the model is to optimize several mines in an integrated fashion, but real size instances are hard to solve due to the combinatorial nature of the problem. We tackle this by solving the relaxation of a tight linear formulation, and we round ...
• Approximation Algorithms for VRP with Stochastic Demands AI翻译
  作者：Gupta, Anupam; Nagarajan, Viswanath; Ravi, R.
  作者单位：Carnegie Mellon University; International Business Machines (IBM); IBM USA; Carnegie Mellon University
  摘要：We consider the vehicle routing problem with stochastic demands (VRPSD). We give randomized approximation algorithms achieving approximation guarantees of 1 + alpha for split-delivery VRPSD, and 2 + alpha for unsplit-delivery VRPSD; here alpha is the best approximation guarantee for the traveling salesman problem. These bounds match the best known for even the respective deterministic problems [Altinkemer, K., B. Gavish. 1987. Heuristics for unequal weight delivery problems with a fixed error ...
• The Knowledge Gradient Algorithm for a General Class of Online Learning Problems AI翻译
  作者：Ryzhov, Ilya O.; Powell, Warren B.; Frazier, Peter I.
  作者单位：University System of Maryland; University of Maryland College Park; Princeton University; Cornell University
  摘要：We derive a one-period look-ahead policy for finite- and infinite-horizon online optimal learning problems with Gaussian rewards. Our approach is able to handle the case where our prior beliefs about the rewards are correlated, which is not handled by traditional multiarmed bandit methods. Experiments show that our KG policy performs competitively against the best-known approximation to the optimal policy in the classic bandit problem, and it outperforms many learning policies in the correlate...