On sums of squares of k-nomials

In 2005, Boman et al. introduced the concept of factor width for a real symmetric positive semidefinite matrix. This is the smallest positive integer k for which the matrix A can be written as A = VVT with each column of V containing at most k non-zeros. The cones of matrices of bounded factor width give a hierarchy of inner approximations to the PSD cone. In the polynomial optimization context, a Gram matrix of a polynomial having factor width k corresponds to the polynomial being a sum of squares of polynomials of support at most k. Recently, Ahmadi and Majumdar [1], explored this connection for case k = 2 and proposed to relax the reliance on polynomials that are sums of squares in semidefinite programming to polynomials that are sums of binomial squares In this paper, we prove some results on the geometry of the cones of matrices with bounded factor widths and their duals, and use them to derive new results on the limitations of certificates of nonnegativity of quadratic forms by sums of k-nomial squares using standard multipliers. In particular we show that they never help for symmetric quadratics, for any quadratic if k = 2, and any quaternary quadratic if k = 3. Furthermore we give some evidence that those are a complete list of such cases. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Introduced in 2005 by Boman et al., factor width for a real symmetric positive semidefinite matrix determines the smallest positive integer k for which the matrix can be expressed as A = VVT with each column of V containing at most k non-zeros. This concept has practical implications in the context of polynomial optimization and the connections between polynomials and sums of squares.