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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Papers
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01 Nov 2009TL;DR: A new greedy algorithm to perform sparse signal reconstruction from signs of signal measurements, i.e., measurements quantized to 1-bit, which demonstrates that combining the principle of consistency with a sparsity prior outperforms approaches that use only consistency or only sparsity priors.
Abstract: This paper presents Matched Sign Pursuit (MSP), a new greedy algorithm to perform sparse signal reconstruction from signs of signal measurements, i.e., measurements quantized to 1-bit. The algorithm combines the principle of consistent reconstruction with greedy sparse reconstruction. The resulting MSP algorithm has several advantages, both theoretical and practical, over previous approaches. Although the problem is not convex, the experimental performance of the algorithm is significantly better compared to reconstructing the signal by treating the quantized measurement as values. Our results demonstrate that combining the principle of consistency with a sparsity prior outperforms approaches that use only consistency or only sparsity priors.
166 citations
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10 Dec 2012TL;DR: It is shown that the problem is NP-hard, and the monotonicity and submodularity of the time constrained influence spread function is proved, and a greedy algorithm with performance guarantees is developed.
Abstract: Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing """"Word-of-Mouth"""" effect in social networks. Influence maximization problem has been extensively studied recently. However, none of the previous work considers the time constraint in the influence maximization problem. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm with performance guarantees. To improve the algorithm scalability, we propose two Influence Spreading Path based methods. Extensive experiments conducted over four public available datasets demonstrate the efficiency and effectiveness of the Influence Spreading Path based methods.
166 citations
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01 Oct 1990TL;DR: A genetic algorithm for solving the traveling salesman problem by genetic algorithms to optimality for traveling salesman problems with up to 442 cities is presented.
Abstract: We present a genetic algorithm for solving the traveling salesman problem by genetic algorithms to optimality for traveling salesman problems with up to 442 cities. Muhlenbein et al. [MGK 88], [MK 89] have proposed a genetic algorithm for the traveling salesman problem, which generates very good but not optimal solutions for traveling salesman problems with 442 and 531 cities. We have improved this approach by improving all basic components of that genetic algorithm. For our experimental investigations we used the traveling salesman problems TSP (i) with i cities for i=137, 202, 229, 318, 431, 442, 666 which were solved to optimality in [CP 80], [GH 89].
164 citations
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TL;DR: The Multi-Terminal Sweep Algorithm (MTS) as mentioned in this paper is a heuristic algorithm for obtaining an approximate solution to the multiple terminal vehicle-dispatch problem, which determines a set of routes by which vehicles from two or more terminals can service a collection of demand points so that the total distance traveled is kept near to the minimum.
Abstract: This paper introduces the Multi-Terminal Sweep Algorithm, a heuristic algorithm for obtaining an approximate solution to the multiple terminal vehicle-dispatch problem. The procedure determines a set of routes by which vehicles from two or more terminals can service a collection of demand points so that the total distance traveled is kept near to the minimum. This solution also satisfies constraints on the vehicle load and on the length of each route. Application of the algorithm to eleven multiple terminal vehicle-dispatch problems shows that near-optimal solutions to large-scale problems can be found in a reasonable amount of computer time.
164 citations
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29 May 2001TL;DR: An intuitive interpretation of this equivalence is given that this problem of traffic grooming to reduce the number of transceivers in optical networks is equivalent to a certain traffic maximization problem and this interpretation is used to derive a greedy algorithm for transceiver minimization.
Abstract: We study the problem of traffic grooming to reduce the number of transceivers in optical networks. We show that this problem is equivalent to a certain traffic maximization problem. We give an intuitive interpretation of this equivalence and use this interpretation to derive a greedy algorithm for transceiver minimization. We discuss implementation issues and present computational results comparing the heuristic solutions with the optimal solutions for several small example networks. For larger networks, the heuristic solutions are compared with known bounds on the optimal solution obtained using integer programming tools.
164 citations