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The Budgeted Maximum Coverage Problem
Approximation Algorithms for Connected Dominating Sets
TLDR
This work considers the more general problem of finding a connected dominating set of a specified subset of vertices and provides a polynomial time algorithm with a (c+1) H(Δ) +c-1 approximation factor, where c is the Steiner approximation ratio for graphs.
Greedy strikes back: improved facility location algorithms
TLDR
It is shown that a simple greedy heuristic combined with the algorithm by Shmoys, Tardos, and Aardal, can be used to obtain an approximation guarantee of 2.408, and a lower bound of 1.463 is proved on the best possible approximation ratio.
Landmarks in Graphs
On Finding Dense Subgraphs
TLDR
This paper focuses on developing fast polynomial time algorithms for several variations of dense subgraph problems for both directed and undirected graphs and shows that the problem is NP-complete and gives fast algorithms to find subgraphs within a factor 2 of the optimum density.
Construction of an efficient overlay multicast infrastructure for real-time applications
TLDR
This paper presents a decentralized scheme that organizes the MSNs into an appropriate overlay structure that is particularly beneficial for real-time applications and iteratively modifies the overlay tree using localized transformations to adapt with changing distribution of MSNs, clients, as well as network conditions.
Algorithms for facility location problems with outliers
TLDR
This paper explores a generalization of various facility location problems to the case when only a specified fraction of the customers are to be served, and provides generalizations of various approximation algorithms to deal with this added constraint.
A clustering scheme for hierarchical control in multi-hop wireless networks
TLDR
This paper presents a clustering scheme to create a hierarchical control structure for multi-hop wireless networks and presents an efficient distributed implementation of the clustering algorithm for a set of wireless nodes to create the set of desired clusters.
Achieving anonymity via clustering
TLDR
This is the first set of algorithms for the anonymization problem where the performance is independent of the anonymity parameter k, and extends the algorithms to allow an ε fraction of points to remain unclustered, i.e., deleted from the anonymized publication.
Dependent rounding and its applications to approximation algorithms
We develop a new randomized rounding approach for fractional vectors defined on the edge-sets of bipartite graphs. We show various ways of combining this technique with other ideas, leading to
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