
Optimal Distributed Covering Algorithms
We present a timeoptimal deterministic distributed algorithm for approx...
read it

A Deterministic Distributed 2Approximation for Weighted Vertex Cover in O( n/ ^2) Rounds
We present a deterministic distributed 2approximation algorithm for the...
read it

Distributed approximation algorithms for maximum matching in graphs and hypergraphs
We describe randomized and deterministic approximation algorithms in Lin...
read it

Efficiently Approximating Vertex Cover on ScaleFree Networks with Underlying Hyperbolic Geometry
Finding a minimum vertex cover in a network is a fundamental NPcomplete...
read it

Approximating the discrete timecost tradeoff problem with bounded depth
We revisit the deadline version of the discrete timecost tradeoff probl...
read it

Largest Weight Common Subtree Embeddings with Distance Penalties
The largest common embeddable subtree problem asks for the largest possi...
read it

Optimal Parametric Search for Path and Tree Partitioning
We present lineartime algorithms for partitioning a path or a tree with...
read it
Optimal Distributed Weighted Set Cover Approximation
We present a timeoptimal deterministic distributed algorithm for approximating a minimum weight vertex cover in hypergraphs of rank f. This problem is equivalent to the Minimum Weight Set Cover Problem in which the frequency of every element is bounded by f. The approximation factor of our algorithm is (f+ϵ). Let Δ denote the maximum degree in the hypergraph. Our algorithm runs in the CONGEST model and requires O(Δ / Δ) rounds, for constants ϵ∈ (0,1] and f∈ N^+. This is the first distributed algorithm for this problem whose running time does not depend on the vertex weights or the number of vertices. Thus adding another member to the exclusive family of provably optimal distributed algorithms. For constant values of f and ϵ, our algorithm improves over the (f+ϵ)approximation algorithm of KuhnMW06 whose running time is O(Δ + W), where W is the ratio between the largest and smallest vertex weights in the graph.
READ FULL TEXT
Comments
There are no comments yet.