Uriel Feige, Marek Karpinski, Michael Langberg

We analyze the addition of a simple local improvement step to various known

randomized approximation algorithms.

Let $\alpha \simeq 0.87856$ denote the best approximation ratio currently

known for the Max Cut problem on general graphs~\cite{GW95}.

We consider a semidefinite relaxation of the Max Cut problem,

round it using the ...
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Sofya Raskhodnikova, Adam Smith

We show that in the bounded degree model for graph property testing,

adaptivity is essential. An algorithm is *non-adaptive* if it makes all queries to the input before receiving any answers. We call a property *non-trivial* if it does not depend only on the degree distribution of the nodes. We ...
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Akash Kumar, C. Seshadhri, Andrew Stolman

Let $G$ be an undirected, bounded degree graph with $n$ vertices. Fix a finite graph $H$, and suppose one must remove $\varepsilon n$ edges from $G$ to make it $H$-minor free (for some small constant $\varepsilon > 0$).

We give an $n^{1/2+o(1)}$-time randomized procedure that, with high probability, finds an ...
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Akash Kumar, C. Seshadhri, Andrew Stolman

with $n$ vertices. Fix a finite graph $H$, and suppose one must remove $\varepsilon n$ edges from $G$ to make it $H$-minor free (for some small constant $\varepsilon > 0$). We give an $n^{1/2+o(1)}$-time randomized procedure that, with high probability, finds an ...
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