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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 2-competitive. We develop a modified greedy algorithm, called Semi-Greedy, and prove that it achieves a competitive ratio of 17=9 1:89. The new algorithm

We revisit the well-known 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

Text-compression 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 on-line heuristic which locally maximizes the compaction ratio. The worst

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

We provide a new simpler approach to the on-line load balancing problem in the case of restricted assignment of temporary weighted tasks. The approach is very general and allows to derive on-line distributed algorithms whose competitive ratio is characterized by some combinatorial properties

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 on-line algorithm can do n/2. We

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 state-of-the-art greedyim techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large real-world 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 conformity-unaware. 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 conformity-aware cascade (c 2) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying

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

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