On sums of squares of $k$-nomials
Jo\~ao Gouveia, Alexander Kova\v{c}ec, Mina Saee

TL;DR
This paper investigates the geometric properties of matrices with bounded factor width and their implications for polynomial nonnegativity certificates, revealing limitations of sum of $k$-nomial squares in certain cases.
Contribution
It provides new geometric insights into cones of matrices with bounded factor width and demonstrates the limitations of sum of $k$-nomial squares for certifying nonnegativity.
Findings
Sum of $k$-nomial squares do not help for symmetric quadratics.
Sum of $k$-nomial squares are ineffective for any quadratic when $k=2$.
Limitations extend to any quaternary quadratic when $k=3$.
Abstract
In 2005, Boman et al introduced the concept of factor width for a real symmetric positive semidefinite matrix. This is the smallest positive integer for which the matrix can be written as with each column of containing at most 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 corresponds to the polynomial being a sum of squares of polynomials of support at most . Recently, Ahmadi and Majumdar, explored this connection for case and proposed to relax the reliance on sum of squares polynomials in semidefinite programming to sum of binomial squares polynomials (sobs; which they call sdsos), for which semidefinite programming can be reduced to second order programming to gain scalability at the cost of…
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