Topic
Discrete optimization
About: Discrete optimization is a research topic. Over the lifetime, 4598 publications have been published within this topic receiving 158297 citations. The topic is also known as: discrete optimisation.
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TL;DR: Reference LA-ARTICLE-1995-002View record in Web of Science Record created on 2004-11-26, modified on 2017-05-10 as mentioned in this paper, created on 2003
73 citations
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TL;DR: An edge‐preserving prior (EPP) is introduced that instead assumes that intensities are piecewise smooth, and a new approach to efficiently compute its Bayesian estimate is proposed.
Abstract: Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a non-convex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.
73 citations
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08 Dec 2003TL;DR: An algorithm for solving multiobjective optimization problems is presented based on PSO through the improvement of the selection manner for global and individual extremum and numerical simulations show the effectiveness of the proposed algorithm.
Abstract: An algorithm for solving multiobjective optimization problems is presented based on PSO through the improvement of the selection manner for global and individual extremum. The search for the Pareto optimal set of multiobjective optimization problems is performed. Numerical simulations show the effectiveness of the proposed algorithm.
72 citations
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TL;DR: Since the presented method combines the merits of traditional optimization algorithms and particle swarm optimization, only a small number of particles is needed to achieve the optimal position after several iterations and shows great advantages in solving engineering optimization problems with expensive black box functions.
Abstract: In engineering applications, computer experiments such as finite element analysis and computational fluid dynamics are often used to model and analyse structural behaviours. In this article, a surrogate-based particle swarm optimization algorithm is proposed for solving optimization problems with expensive black box functions. An approximate optimization problem in which the black box functions are replaced by the hybrid surrogate models is efficiently solved to search and adjust the global optimum position during the iterative process. Since the presented method combines the merits of traditional optimization algorithms and particle swarm optimization, only a small number of particles is needed to achieve the optimal position after several iterations. Therefore, the method shows great advantages in solving engineering optimization problems with expensive black box functions. Several examples are presented to demonstrate the feasibility and effectiveness of the proposed method.
72 citations
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TL;DR: This paper gives the background, basic concepts, and fundamental formulation of MRF, and focuses on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.
Abstract: Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.
72 citations