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Online submodular welfare maximization: Greedy is optimal

by Michael Kapralov, Ian Post, Jan Vondrák

"... We prove that no online algorithm (even randomized, against an oblivious adversary) is better than 1/2-competitive for welfare maximization with coverage valuations, unless NP = RP. Since the Greedy algorithm is known to be 1/2-competitive for monotone submodular valuations, of which coverage is a s ..."

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We prove that no online algorithm (even randomized, against an oblivious adversary) is better than 1/2-competitive for welfare maximization with coverage valuations, unless NP = RP. Since the Greedy algorithm is known to be 1/2-competitive for monotone submodular valuations, of which coverage is a

Greedy Online Frequency Allocation in Cellular Networks

by Wun-tat Chan, Francis Y. L. Chin, Deshi Ye, Yong Zhang, Hong Zhu

"... The online frequency allocation problem for cellular networks has been well studied in these years. Given a mobile telephone network, whose geographical coverage area is divided into cells, phone calls are served by assigning frequencies to them, and no two calls emanating from the same or neighbori ..."

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or neighboring cells are assigned the same frequency. Assuming an online setting that the calls arrive one by one, the problem is to minimize the span of the frequencies used. In this paper, we study the greedy approach for the online frequency allocation problem, which assigns the minimal available frequency

On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm

by Goran Dimić, Nicholas D. Sidiropoulos, Senior Member - IEEE Trans. Signal Processing , 2005

"... Abstract—This paper considers the problem of simultaneous multiuser downlink beamforming. The idea is to employ a transmit antenna array to create multiple “beams ” directed toward the individual users, and the aim is to increase throughput, measured by sum capacity. In particular, we are interested ..."

Abstract - Cited by 109 (1 self) - Add to MetaCart

are interested in the practically important case of more users than transmit antennas, which requires user selection. Optimal solutions to this problem can be prohibitively complex for online implementation at the base station and entail so-called Dirty Paper (DP) precoding for known interference. Suboptimal

Greedy Online Algorithms for Routing Permanent Virtual Circuits

by Jessen T. Havill, Weizhen Mao - Networks , 1999

"... We analyze the competitive ratio of two greedy online algorithms for routing permanent virtual circuits in a network with arbitrary topology and uniform capacity links. We show that the competitive ratio of the first algorithm, with respect to network congestion, is in \Omega\Gamma p Dm) and O( p ..."

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We analyze the competitive ratio of two greedy online algorithms for routing permanent virtual circuits in a network with arbitrary topology and uniform capacity links. We show that the competitive ratio of the first algorithm, with respect to network congestion, is in \Omega\Gamma p Dm) and O( p

Efficient influence maximization in social networks

by Wei Chen, Yajun Wang, Siyu Yang - In Proc. of ACM KDD , 2009

"... Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of [5 ..."

Abstract - Cited by 197 (18 self) - Add to MetaCart

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm

Tight bounds for selfish and greedy load balancing

by Ioannis Caragiannis, Michele Flammini, Christos Kaklamanis, Panagiotis Kanellopoulos, Luca Moscardelli - ICALP 2006. LNCS , 2006

"... Abstract. We study the load balancing problem in the context of a set of clients each wishing to run a job on a server selected among a subset of permissible servers for the particular client. We consider two different scenarios. In selfish load balancing, each client is selfish in the sense that it ..."

Abstract - Cited by 43 (6 self) - Add to MetaCart

. This gives rise to greedy online solutions. The aim of this paper is to determine how much the quality of load balancing is affected by selfishness and greediness. We characterize almost completely the impact of selfishness and greediness in load balancing by presenting new and improved, tight or almost

GREEDY ON-LINE FILE TRANSFER ROUTING*

by unknown authors

"... ABSTRACT We study the performance of a simple greedy on-linealgorithm for routing large file transfers in a network. The goal of the algorithm is to minimize network con-gestion. We show that the competitive ratio of the greedy algorithm is O \Gamma L min \Phi \Lambda * , I\Psi log(nI)\Delta where L ..."

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ABSTRACT We study the performance of a simple greedy on-linealgorithm for routing large file transfers in a network. The goal of the algorithm is to minimize network con-gestion. We show that the competitive ratio of the greedy algorithm is O \Gamma L min \Phi \Lambda * , I\Psi log(nI)\Delta where