Convex Graph Invariants

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Convex Graph Invariants
The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. In this paper we study convex graph invariants, which are graph invariants that are convex functions of the adjacency matrix of a graph. Some examples include functions of a graph such as the maximum degree, the MAXCUT value (and its semidefinite relaxation), and spectral invariants such as the sum of the k largest eigenvalues. Such functions can be used to construct convex sets that impose various structural constraints on graphs, and thus provide a unified framework for solving a number of interesting graph problems via convex optimization. We give a representation of all convex graph invariants in terms of certain elementary invariants, and describe methods to compute or approximate convex graph invariants tractably. We also compare convex and non-convex invariants, and discuss connections to robust optimization. Finall...
Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. W
Added 22 Mar 2011
Updated 22 Mar 2011
Type Journal
Year 2010
Where CORR
Authors Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky
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