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513
On the Performance of Greedy Algorithms in Packet Buffering
"... We study a basic buffer management problem that arises in network switches. Consider m input ports, each of which is equipped with a buffer (queue) of limited capacity. Data packets arrive online and can be stored in the buffers if space permits; otherwise packet loss occurs. In each time step the s ..."
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for deterministic and randomized online algorithms. In the second part of the paper we present the first deterministic online algorithm that is better than 2competitive. We develop a modified greedy algorithm, called SemiGreedy, and prove that it achieves a competitive ratio of 17=9 1:89. The new algorithm
On the performance of greedy algorithms for energy minimization
, 2010
"... We revisit the wellknown greedy algorithm for scheduling independent jobs on parallel processors, with the objective of energy minimization. We assess the performance of the online version, as well as the performance of the offline version, which sorts the jobs by nonincreasing size before executio ..."
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Cited by 2 (1 self)
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We revisit the wellknown greedy algorithm for scheduling independent jobs on parallel processors, with the objective of energy minimization. We assess the performance of the online version, as well as the performance of the offline version, which sorts the jobs by nonincreasing size before
The Fractional Greedy Algorithm for Data Compression
"... Textcompression problems are considered where substrings are substituted by code words according to a static dictionary such that the original text is encoded by a shorter code sequence. We introduce a new efficient online heuristic which locally maximizes the compaction ratio. The worstcase be ..."
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Textcompression problems are considered where substrings are substituted by code words according to a static dictionary such that the original text is encoded by a shorter code sequence. We introduce a new efficient online heuristic which locally maximizes the compaction ratio. The worst
Scalable Influence Maximization for Prevalent Viral Marketing in LargeScale Social Networks
"... Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling preval ..."
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Cited by 183 (14 self)
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prevalent viral marketing in largescale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a
Online load balancing made simple: Greedy strikes back
 Journal of Discrete Algorithms
, 2003
"... We provide a new simpler approach to the online load balancing problem in the case of restricted assignment of temporary weighted tasks. The approach is very general and allows to derive online distributed algorithms whose competitive ratio is characterized by some combinatorial properties of the ..."
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Cited by 3 (1 self)
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We provide a new simpler approach to the online load balancing problem in the case of restricted assignment of temporary weighted tasks. The approach is very general and allows to derive online distributed algorithms whose competitive ratio is characterized by some combinatorial properties
Greedy Algorithms for onLine SetCovering and Related Problems
, 2006
"... We study the following online model for setcovering: elements of a ground set of size n arrive onebyone and with any such element c i , arrives also the name of some set S i 0 containing c i and covering the most of the uncovered ground setelements (obviously, these elements have not been yet r ..."
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Cited by 1 (0 self)
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revealed). For this model we analyze a simple greedy algorithm consisting of taking S i 0 into the cover, only if c i is not already covered. We prove that the competitive ratio of this algorithm is # n and that it is asymptotically optimal for the model dealt, since no online algorithm can do n/2. We
CINEMA: ConformityAware Greedy Algorithm for Influence Maximization in Online Social Networks
, 2013
"... Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by stateoftheart greedyim techniques, they suffer from two key limitations. Firstly, they are inefficient as th ..."
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Cited by 4 (0 self)
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Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by stateoftheart greedyim techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large realworld networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existingim techniques are conformityunaware. That is, they only utilize an individual’s ability to influence another but ignores conformity (a person’s inclination to be influenced) of the individuals. In this paper, we propose a novel conformityaware cascade (c 2) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying
Extending Greedy Multicast Routing to Delay Sensitive Applications
, 1999
"... Given a weighted undirected graph G(V; E) and a subset R of V , a Steiner tree is a subtree of G that contains each vertex in R. In this paper, we present an online algorithm for nding a Steiner tree that simultaneously approximates the shortest path tree and the minimum weight Steiner tree, when ..."
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Cited by 11 (2 self)
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Given a weighted undirected graph G(V; E) and a subset R of V , a Steiner tree is a subtree of G that contains each vertex in R. In this paper, we present an online algorithm for nding a Steiner tree that simultaneously approximates the shortest path tree and the minimum weight Steiner tree
Learning Transition Dynamics in MDPs with Online Regression and Greedy Feature Selection∗
"... We present an approach to reinforcement learning in which the system dynamics are modelled using online linear regression between feature spaces, and a compact feature representation for the dynamics model is built incrementally using greedy feature selection. Candidate features are built online usi ..."
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We present an approach to reinforcement learning in which the system dynamics are modelled using online linear regression between feature spaces, and a compact feature representation for the dynamics model is built incrementally using greedy feature selection. Candidate features are built online
Results 21  30
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513