A real polynomial is a sum of squares polynomial if for real polynomials . Such a representation certifies global nonnegativity. If is the vector of relevant monomials, the identity with a positive semidefinite matrix encodes a sum of squares through a Gram matrix. Equating coefficients is linear in , so finding a certificate is a semidefinite program.
If a degree- homogeneous polynomial is a sum of squares polynomial, it has a representation as squares of degree- homogeneous polynomials. In any sum of squares, the highest-degree terms cannot cancel, since their homogeneous part is itself a sum of squares. Likewise the lowest nonzero degree cannot cancel. Thus every nonzero summand has only degree .
A polynomial invariant under changing the sign of each coordinate can have its sum of squares representation averaged over independent Rademacher random variables . Distinct parity patterns of monomials have zero cross terms because vanishes when some exponent is odd. For a quadratic homogeneous polynomialthis givesThis identity separates the even monomials from each distinct two-coordinate parity pattern.
For a real symmetric matrix , set . ThenFor sufficiency, factor . Its contribution is a sum of squares of linear combinations of , while the contribution of is . For necessity, use a homogeneous sum of squares representation and sign averaging of a sum of squares. Writing each quadratic summand with coefficients gives , and for . Comparing coefficients gives .
This is the basic semidefinite programming certificate of copositivity discussed in Parrilo's paper on matrix copositivity.
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