- Positive Semidefinite Matrix Completion, Universal Rigidity and the Strong Arnold Property Monique Laurent(moniquecwi.nl) Antonios Varvitsiotis(A.Varvitsiotiscwi.nl) Abstract: 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 rank 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 $\nu^=(\cdot)$. Keywords: Matrix completion, tensegrity framework, universal rigidity, semidefinite programming, Strong Arnold Property, nondegeneracy Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Citation: Download: [PDF]Entry Submitted: 02/14/2013Entry Accepted: 02/14/2013Entry Last Modified: 02/14/2013Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.