M. Naor. Constructing Ramsey graphs from small probability spaces. IBM Research Report RJ 8810, 1992. Home/Search Document Details and Download
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Approximating Hyper-Rectangles: Learning and Pseudo-random Sets - Auer, Long, al. (1997) (18 citations) (Correct)
....usually help show that an (s; t ff ; n) graph exists, where ff 1 is some constant. Since there are still enormous gaps between the nonconstructive and known constructive bounds for Ramsey type graphs, we just follow the basic approach of using (17) for our simple constructive method. See [30] for an approach to constructing Ramsey graphs via derandomization techniques, which has a different motivation than our present one. It can be checked that the method of conditional probabilities can be used to constructivize the simple approach above, leading to a deterministic sequential ....
M. Naor. Constructing Ramsey graphs from small probability spaces. IBM Research Report RJ 8810, 1992. 21
The Probabilistic Method Yields Deterministic Parallel.. - Motwani, Naor, Naor (1989) (46 citations) (Correct)
....X S Pr[A S ] can be used as a pessimistic estimator, and using the techniques of Section 4.2 it can be evaluated in polylogarithmic time, if we are provided with n log n processors. Recently, a different construction of Ramsey graphs that uses limited independence was suggested in [Na]. 11.2 Independent sets in sparse hypergraphs Alon, Babai Itai [ABI] provided an algorithm for finding a large independent set in a d uniform hypergraph H = V; E) In a d uniform hypergraph, each contains exactly d vertices) A set W V is independent if W contains no edge. Their ....
M. Naor, "Constructing Ramsey graphs from small probability spaces", IBM Research Report RJ 8810, 1992.
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M. Naor, Constructing Ramsey Graphs from Small Probability Spaces, IBM Research Report RJ 8810, 1992.
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M. Naor. Constructing Ramsey graphs from small probability spaces. IBM Research Report RJ 8810, 1992.
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M. Naor. Constructing Ramsey graphs from small probability spaces. IBM Research Report RJ 8810, 1992.
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