This paper addresses the following three topics: positive semidefinite (psd) matrix completions, universal rigidity of frameworks, and the Strong Arnold Property (SAP). We show some strong connections among these topics, using semidefinite programming as unifying theme. Our main contribution is a sufficient condition for constructing partial psd matrices which admit a unique completion to a full psd matrix. Such partial matrices are an essential tool in the study of the Gram dimension $\gd(G)$ of a graph G, a recently studied graph parameter related to the low psd matrix completion problem. Additionally, we derive an elementary proof of Connelly's sufficient condition for universal rigidity of tensegrity frameworks and we investigate the links between these two sufficient conditions. We also give a geometric characterization of psd matrices satisfying the Strong Arnold Property in terms of nondegeneracy of an associated semidefinite program, which we use to establish some links between the Gram dimension $\gd(\cdot)$ and the Colin de Verdi\`ere type graph parameter ν=(⋅).
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Cornell University Library e-Print archive
Semidefinite programming and combinatorial optimization
Networks and Optimization

Laurent, M., & Varvitsiotis, A. (2013). Positive semidefinite matrix completion, universal rigidity and the Strong Arnold Property. e-Print archive. Cornell University Library .