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.
Papers published on a yearly basis
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TL;DR: This work presents an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches and provides an extensive empirical investigation of network architectures and model parameters.
Abstract: Motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network’s output forms the data term for discrete MAP inference in a pairwise Markov random field. We provide an extensive empirical investigation of network architectures and model parameters. At the time of submission, our method ranks second on the challenging MPI Sintel test set.
75 citations
TL;DR: This paper proposes a new heuristic algorithm for the optimization of a performance measure of a simulation model constrained under a discrete decision space by basing portions of the search procedure on inferred statistical knowledge of the system instead of using a strict random search.
74 citations
TL;DR: Existence, uniqueness and characterizing properties are given for a class of constrained minimization problems in real Euclidean space whose solutions are generalized splines, which are called discrete splines.
Abstract: Existence, uniqueness and characterizing properties are given for a class of constrained minimization problems in real Euclidean space. These problems are the discrete analogues of minimization problems in Banach space whose solutions are generalized splines. Solutions of these discrete problems, which are called discrete splines, can be obtained by algorithms of mathematical programming.
74 citations
TL;DR: This study focused on analysis of discrete ABC with neighborhood operator for well-known traveling salesman problem and different discrete neighborhood operators are replaced with solution updating equations of the basic ABC.
Abstract: The artificial bee colony (ABC) algorithm, inspired intelligent behaviors of real honey bee colonies, was introduced by Karaboga for numerical function optimization. The basic ABC has high performance and accuracy, if the solution space of the problem is continuous. But when the solution space of the problem is discrete, the basic ABC algorithm should be modified to solve this class optimization problem. In this study, we focused on analysis of discrete ABC with neighborhood operator for well-known traveling salesman problem and different discrete neighborhood operators are replaced with solution updating equations of the basic ABC. Experimental computations show that the promising results are obtained by the discrete version of the basic ABC and which neighborhood operator is better than the others. Also, the results obtained by discrete ABC were enriched with 2- and 3-opt heuristic approaches in order to increase quality of the solutions.
74 citations
74 citations