Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: The overall performance of the GA for the QAP improves by using greedy methods but not their overuse, and the use of several possible enhancements to GAs are investigated and illustrated using the Quadratic Assignment Problem, one of the hardest nut in the field of combinatorial optimization.
336 citations
TL;DR: An effective heuristic that combines a genetic algorithm with a local tour improvement heuristic for the generalized traveling salesman problem, which is competitive with other heuristics published to date in both solution quality and computation time.
332 citations
TL;DR: This work considers the problem of clustering dynamic point sets in a metric space and proposes a model called incremental clustering which is based on a careful analysis of the requirements of the information retrieval application, and which should also be useful in other applications.
Abstract: Motivated by applications such as document and image classification in information retrieval, we consider the problem of clustering dynamic point sets in a metric space. We propose a model called incremental clustering which is based on a careful analysis of the requirements of the information retrieval application, and which should also be useful in other applications. The goal is to efficiently maintain clusters of small diameter as new points are inserted. We analyze several natural greedy algorithms and demonstrate that they perform poorly. We propose new deterministic and randomized incremental clustering algorithms which have a provably good performance, and which we believe should also perform well in practice. We complement our positive results with lower bounds on the performance of incremental algorithms. Finally, we consider the dual clustering problem where the clusters are of fixed diameter, and the goal is to minimize the number of clusters.
331 citations
20 Oct 2012
TL;DR: This work presents a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige et al.
Abstract: We consider the Unconstrained Sub modular Maximization problem in which we are given a non-negative sub modular function $f:2^{N}\right arrow R^+$, and the objective is to find a subset $S\subseteq N$ maximizing $f(S)$. This is one of the most basic sub modular optimization problems, having a wide range of applications. Some well known problems captured by Unconstrained Sub modular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of $1/2$, thus matching the known hardness result of Feige et al. Our algorithm is based on an adaptation of the greedy approach which exploits certain symmetry properties of the problem. Our method might seem counterintuitive, since it is known that the greedy algorithm fails to achieve any bounded approximation factor for the problem.
331 citations
TL;DR: Gao et al. as mentioned in this paper developed a theoretical approach and a greedy algorithm to study target control, the ability to efficiently control a preselected subset of nodes in complex networks, which has numerous applications in natural and technological systems.
Abstract: Network controllability has numerous applications in natural and technological systems. Here, Gao et al. develop a theoretical approach and a greedy algorithm to study target control—the ability to efficiently control a preselected subset of nodes—in complex networks.
331 citations