Randomized algorithms
Towards an analysis of local optimization algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Finding a large hidden clique in a random graph
proceedings of the eighth international conference on Random structures and algorithms
Go with the winners for graph bisection
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Finding and certifying a large hidden clique in a semirandom graph
Random Structures & Algorithms
Hiding Cliques for Cryptographic Security
Designs, Codes and Cryptography
Parallel 'Go with the Winners' Algorithms in the LogP Model
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Clique Is Hard to Approximate within n1-o(1)
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
Approximating the Independence Number and the Chromatic Number in Expected Polynominal Time
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
Improved Lower Bounds for the Randomized Boppana-Halldórsson Algorithm for MAXCLIQUE
COCOON '95 Proceedings of the First Annual International Conference on Computing and Combinatorics
`Go with the winners' Generators with Applications to Molecular Modeling
RANDOM '97 Proceedings of the International Workshop on Randomization and Approximation Techniques in Computer Science
Clique is hard to approximate within n1-
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
"Go with the winners" algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
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This paper analyzes the performance of the Go with the Winners algorithm (GWTW) of Aldous and Vazirani [1] on random instances of the clique problem. In particular, we consider the uniform distribution on the set of all graphs with n ∈ IN vertices. We prove a lower bound of nΩ(log n) and a matching upper bound on the time needed by GWTW to find a clique of size (1 + Ɛ) log n (for any constant Ɛ 0). We extend the lower bound result to other distributions, under which graphs are guaranteed to have large cliques.