Mikael Onsjö

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We study the concept of propagation connectivity on random 3-uniform hyper-graphs. This concept is inspired by a simple propagation algorithm for solving instances of certain constraint satisfaction problems. We derive upper and lower bounds for the propagation connectivity threshold. Our proof is based on a kind of large deviations analysis of a(More)
Based on the Belief Propagation Method, we propose simple and determin-istic algorithms for some NP-hard graph partitioning problems, such as the Most Likely Partition problem and the Graph Bisection problem. These algorithms run in O(n + m) or O((n + m) log n) time on graphs with n vertices and m edges. For their average case analysis, we consider the(More)
We consider the problem of computing f m (f m−1 (· · · f 1 (x) · · ·)) where each function f i : R n → R n can be broken up in pairs so that the computation at, e.g., indices k and l involve only the vales of the argument at positions k and l. That is, f j (u)) k def = f + j (u k , u l) and so on. This generalizes " butterfly " algorithms, such as Radix-2(More)
We study the concept of propagation connectivity on random 3-uniform hypergraphs. This concept is inspired by a simple linear time algorithm for solving instances of certain constraint satisfaction problems. We derive upper and lower bounds for the propagation connectivity threshold, and point out some algorithmic implications.
1 Problem Description Bisection Problem is to patition a given graph G into subgraphs G 1 and G 2 of equal size with minimum number of cut edges, i.e., edges between G 1 and G 2. This problem has been studied extensively by many researchers [1, 2, 4], and several algorithms have been proposed and analyzed both mathematically and experimentally. Here we(More)
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