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作者：Li, Haidong; Lam, Henry; Peng, Yijie
作者单位：Peking University; Columbia University; Peking University
摘要：We consider a simulation optimization problem for context-dependent decision making. Under a Gaussian mixture model-based Bayesian framework, we develop a dynamic sampling policy to maximize the worst-case probability of correctly selecting the best design over all contexts, which utilizes both global clustering information and local performance information. In particular, we design a computationally efficient approximation method to learn these sources of information, thereby leading to an im...
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作者：Gooty, Radhakrishna Tumbalam; Agrawal, Rakesh; Tawarmalani, Mohit
作者单位：Purdue University System; Purdue University; Purdue University System; Purdue University
摘要：In this paper, we describe the first mixed-integer nonlinear programming (MINLP)-based solution approach that successfully identifies the most energy-efficient distillation configuration sequence for a given separation. Current sequence design strategies are largely heuristic. The rigorous approach presented here can help reduce the significant energy consumption and consequent greenhouse gas emissions by separation processes. First, we model discrete choices using a formulation that is provab...
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作者：Braverman, Anton; Dai, J. G.; Fang, Xiao
作者单位：Northwestern University; Cornell University; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; 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...
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作者：Chen, Zhongzhu; Fampa, Marcia; Lee, Jon
作者单位：University of Michigan System; University of Michigan; Universidade Federal do Rio de Janeiro
摘要：The maximum-entropy sampling problem is the NP-hard problem of maximizing the (log) determinant of an order-s principal submatrix of a given order n covariance matrix C. Exact algorithms are based on a branch-and-bound framework. The problem has wide applicability in spatial statistics and in particular in environmental monitoring. Probably the best upper bound for the maximum empirically is Anstreicher???s scaled ???linx??? bound. An earlier methodology for potentially improving any upper-bou...
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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...
