Papers › Sum-of-squares chordal decomposition of polynomial matrix inequalities

Sum-of-squares chordal decomposition of polynomial matrix inequalities

22 Jul 2020arXiv:2007.11410links table onlyarchive 2025-07-28

Yang Zheng, Giovanni Fantuzzi

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We prove decomposition theorems for sparse positive (semi)definite polynomial matrices that can be viewed as sparsity-exploiting versions of the Hilbert--Artin, Reznick, Putinar, and Putinar--Vasilescu Positivstellens\"atze. First, we establish that a polynomial matrix P(x) with chordal sparsity is positive semidefinite for all x∈ℝⁿ if and only if there exists a sum-of-squares (SOS) polynomial σ(x) such that σP is a sum of sparse SOS matrices. Second, we show that setting σ(x)=(x₁² + ⋯+ xₙ²)^ν for some integer ν suffices if P is homogeneous and positive definite globally. Third, we prove that if P is positive definite on a compact semialgebraic set 𝒦={x:g₁(x)≥0,…,gₘ(x)≥0} satisfying the Archimedean condition, then P(x) = S₀(x) + g₁(x)S₁(x) + ⋯+ gₘ(x)Sₘ(x) for matrices Sᵢ(x) that are sums of sparse SOS matrices. Finally, if 𝒦 is not compact or does not satisfy the Archimedean condition, we obtain a similar decomposition for (x₁² + …+ xₙ²)^ν P(x) with some integer ν≥0 when P and g₁,…,gₘ are homogeneous of even degree. Using these results, we find sparse SOS representation theorems for polynomials that are quadratic and correlatively sparse in a subset of variables, and we construct new convergent hierarchies of sparsity-exploiting SOS reformulations for convex optimization problems with large and sparse polynomial matrix inequalities. Numerical examples demonstrate that these hierarchies can have a significantly lower computational complexity than traditional ones.

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aeroimperial-optimization/sos-chordal-decomposition-pmi officialmentioned in papermentioned on GitHub report
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