A Generic Algorithm for Approximately Solving Stochastic Graph Optimization Problems

2009 ◽  
pp. 89-103
Author(s):  
Ei Ando ◽  
Hirotaka Ono ◽  
Masafumi Yamashita
Keyword(s):  
Optimization Problems ◽  
Generic Algorithm ◽  
Graph Optimization ◽  
Stochastic Graph

scholarly journals Priority algorithms for graph optimization problems

2010 ◽  
Vol 411 (1) ◽  
pp. 239-258 ◽  
Author(s):  
Allan Borodin ◽  
Joan Boyar ◽  
Kim S. Larsen ◽  
Nazanin Mirmohammadi
Keyword(s):  
Optimization Problems ◽  
Priority Algorithms ◽  
Graph Optimization

Graph optimization problems on a Bethe lattice

1989 ◽  
Vol 54 (1-2) ◽  
pp. 477-493 ◽  
Author(s):  
Mário J. de Oliveira
Keyword(s):  
Optimization Problems ◽  
Bethe Lattice ◽  
Graph Optimization

scholarly journals Self-Stabilizing Global Optimization Algorithms for Large Network Graphs

2005 ◽  
Vol 1 (3-4) ◽  
pp. 329-344 ◽  
Author(s):  
Wayne Goddard ◽  
Stephen T. Hedetniemi ◽  
David P. Jacobs ◽  
Pradip K. Srimani
Keyword(s):  
Global Optimization ◽  
Distributed Algorithms ◽  
Optimization Problems ◽  
Dominating Set ◽  
Optimization Algorithms ◽  
Large Network ◽  
Shared Variable ◽  
Large Networks ◽  
Total Dominating Set ◽  
Graph Optimization

The paradigm of self-stabilization provides a mechanism to design efficient localized distributed algorithms that are proving to be essential for modern day large networks of sensors. We provide self-stabilizing algorithms (in the shared-variable ID-based model) for three graph optimization problems: a minimal total dominating set (where every node must be adjacent to a node in the set) and its generalizations, a maximal k-packing (a set of nodes where every pair of nodes are more than distance k apart), and a maximal strong matching (a collection of totally disjoint edges).

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