Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: It is shown in the article that the size of the infeasible region defined by solutions with subtours dominates that of a feasible region in the asymmetric traveling salesman problem.
104 citations
TL;DR: An extension of the Lin-Kernighan local search algorithm for the solution of the asymmetric traveling salesman problem is presented and computational results suggest that the heuristic is feasible for fairly large instances.
Abstract: We present an extension of the Lin-Kernighan local search algorithm for the solution of the asymmetric traveling salesman problem. Computational results suggest that our heuristic is feasible for fairly large instances. We also present some theoretical results which guided our design of the heuristic.
103 citations
TL;DR: A utility function that measures the quality of proposed sensing locations, gives a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map is developed.
103 citations
TL;DR: This study uses a greedy algorithm to select and configure base station locations and compares the ability of four state-of-the-art multiple objective genetic algorithms to find an optimal ordering of potential base stations.
Abstract: The antenna placement problem, or cell planning problem, involves locating and configuring infrastructure for cellular wireless networks. From candidate site locations, a set needs to be selected against objectives relating to issues such as financial cost and service provision. This is an NP-hard optimization problem and consequently heuristic approaches are necessary for large problem instances. In this study, we use a greedy algorithm to select and configure base station locations. The performance of this greedy approach is dependent on the order in which the candidate sites are considered. We compare the ability of four state-of-the-art multiple objective genetic algorithms to find an optimal ordering of potential base stations. Results and discussion on the performance of the algorithms are provided.
103 citations
01 Jan 2009
TL;DR: The authors discuss text summarization in terms of maximum coverage problem and its variant, and explore some decoding algorithms including the ones never used in this summarization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-and-bound method.
Abstract: We discuss text summarization in terms of maximum coverage problem and its variant. We explore some decoding algorithms including the ones never used in this summarization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-andbound method. On the basis of the results of comparative experiments, we also augment the summarization model so that it takes into account the relevance to the document cluster. Through experiments, we showed that the augmented model is superior to the best-performing method of DUC’04 on ROUGE-1 without stopwords.
103 citations