Disjunctive Sum of Squares

2026-05-27

AI Summary

This work addresses the verification of polynomial nonnegativity and optimization over compact semialgebraic sets. It proposes a disjunctive sum-of-squares approach that constructs a convergent hierarchy of lower bounds using multiple low-degree algebraic identities, each yielding semidefinite constraints of fixed size and solvable in parallel, without requiring additional optimization steps. The core contributions include two novel disjunctive Positivstellensatz theorems—one facilitating low-degree certificates of nonnegativity and the other enabling the construction of the hierarchy—and an extension of the framework to matrix copositivity testing. Experimental results demonstrate that the method is both effective and scalable across polynomial optimization, copositivity verification, and combinatorial optimization problems.

Abstract

We introduce the concept of disjunctive sum of squares for certifying nonnegativity of polynomials. Unlike the popular sum of squares approach where nonnegativity is certified by a single algebraic identity, the disjunctive sum of squares approach certifies nonnegativity with multiple algebraic identities which can be found in parallel. Our main result is a disjunctive Positivstellensatz proving that we can keep the degree of each algebraic identity as low as the degree of the polynomial whose nonnegativity is in question. Based on this result, we construct a semidefinite programming based converging hierarchy of lower bounds for the problem of minimizing a polynomial over a compact basic semialgebraic set, where the size of the largest semidefinite constraint is fixed throughout the hierarchy. We further prove a second disjunctive Positivstellensatz which leads to an optimization-free hierarchy for polynomial optimization. We specialize this result to the problem of proving copositivity of matrices. Finally, we describe how the disjunctive sum of squares approach can be combined with a branch-and-bound algorithm and we present numerical experiments on polynomial, copositive, and combinatorial optimization problems.

Problem

• polynomial nonnegativity
• sum of squares
• Positivstellensatz
• polynomial optimization
• copositivity

Innovation

• disjunctive sum of squares
• Positivstellensatz
• semidefinite programming
• polynomial optimization
• copositivity