The number of matchings in random regular graphs and bipartite graphs
Journal of Combinatorial Theory Series B
Problems and results in combinatorial analysis and graph theory
Discrete Mathematics - First Japan Conference on Graph Theory and Applications
Induced matchings in bipartite graphs
Discrete Mathematics - In memory of Tory Parsons
Discrete Mathematics
Average-case analysis of algorithms for matchings and related problems
Journal of the ACM (JACM)
Maximum induced matchings in graphs
Discrete Mathematics
Maximum matchings in sparse random graphs: Karp-Sipser revisited
Random Structures & Algorithms
Fast distributed construction of small k-dominating sets and applications
Journal of Algorithms
An Efficient Algorithm for Finding a Maximum Weight k-Independent Set on Trapezoid Graphs
Computational Optimization and Applications
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Maximum induced matchings of random cubic graphs
Journal of Computational and Applied Mathematics - Special issue: Probabilistic methods in combinatorics and combinatorial optimization
Packing Edges in Random Regular Graphs
MFCS '02 Proceedings of the 27th International Symposium on Mathematical Foundations of Computer Science
On the $b$-Independence Number of Sparse Random Graphs
Combinatorics, Probability and Computing
Tighter approximations for maximum induced matchings in regular graphs
WAOA'05 Proceedings of the Third international conference on Approximation and Online Algorithms
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In this paper we consider the problem of finding large collections of vertices and edges satisfying particular separation properties in random regular graphs of degree r, for each fixed r ≥ 3. We prove both constructive lower bounds and combinatorial upper bounds on the maximal sizes of these sets. The lower bounds are proved by analyzing a class of algorithms that return feasible solutions for the given problems. The analysis uses the differential equation method proposed by Wormald [Lectures on Approximation and Randomized Algorithms, PWN, Wassaw, 1999, pp. 239–298]. The upper bounds are proved by direct combinatorial means. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2008