Low-Rank Positive Approximants of Symmetric Matrices

DOI: 10.4236/alamt.2014.43015   PDF   HTML     2,582 Downloads   3,263 Views   Citations


Given a symmetric matrix X, we consider the problem of finding a low-rank positive approximant of X. That is, a symmetric positive semidefinite matrix, S, whose rank is smaller than a given positive integer, , which is nearest to X in a certain matrix norm. The problem is first solved with regard to four common norms: The Frobenius norm, the Schatten p-norm, the trace norm, and the spectral norm. Then the solution is extended to any unitarily invariant matrix norm. The proof is based on a subtle combination of Ky Fan dominance theorem, a modified pinching principle, and Mirsky minimum-norm theorem.

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Dax, A. (2014) Low-Rank Positive Approximants of Symmetric Matrices. Advances in Linear Algebra & Matrix Theory, 4, 172-185. doi: 10.4236/alamt.2014.43015.

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The authors declare no conflicts of interest.


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