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Let S = {x ∈ R : g1(x) ≥ 0, · · · , gm(x) ≥ 0} be a semialgebraic set defined by multivariate polynomials gi(x). Assume S is convex, compact and has nonempty interior. Let Si = {x ∈ R : gi(x) ≥ 0}, and ∂S (resp. ∂Si) be the boundary of S (resp. Si). This paper, as does the subject of semidefinite programming (SDP), concerns Linear Matrix Inequalities(More)
This paper studies the so-called bi-quadratic optimization over unit spheres min x∈Rn,y∈Rm ∑ 1≤i,k≤n, 1≤j,l≤m bijklxiyjxkyl subject to ‖x‖ = 1, ‖y‖ = 1. We show that this problem is NP-hard and there is no polynomial time algorithm returning a positive relative approximation bound. After that, we present various approximation methods based on semidefinite(More)
Let S = {x ∈ R | g1(x) ≥ 0, . . . , gm(x) ≥ 0} be a basic closed semialgebraic set defined by real polynomials gi. Putinar’s Positivstellensatz says that, under a certain condition stronger than compactness of S, every real polynomial f positive on S posesses a representation f = P m i=0 σigi where g0 := 1 and each σi is a sum of squares of polynomials.(More)
A method is proposed for finding the global minimum of a multivariate polynomial via sum of squares (SOS) relaxation over its gradient variety. That variety consists of all points where the gradient is zero and it need not be finite. A polynomial which is nonnegative on its gradient variety is shown to be SOS modulo its gradient ideal, provided the gradient(More)
Given a semidefinite program, specified by matrices with rational entries, each coordinate of its optimal solution is an algebraic number. We study the degree of the minimal polynomials of these algebraic numbers. Geometrically, this degree counts the critical points attained by a linear functional on a fixed rank locus in a linear space of symmetric(More)
This paper studies the representation of a positive polynomial f(x) on a noncompact semialgebraic set S = {x ∈ R : g1(x) ≥ 0, · · · , gs(x) ≥ 0} modulo its KKT (Karush-KuhnTucker) ideal. Under the assumption that the minimum value of f(x) on S is attained at some KKT point, we show that f(x) can be represented as sum of squares (SOS) of polynomials modulo(More)
We formulate the sensor network localization problem as finding the global minimizer of a quartic polynomial. Then sum of squares (SOS) relaxations can be applied to solve it. However, the general SOS relaxations are too expensive to implement for large problems. Exploiting the special features of this polynomial, we propose a new structured SOS relaxation,(More)
Abstract. A set S ⊆ R is called to be Semidefinite (SDP) representable if S equals the projection of a set in higher dimensional space which is describable by some Linear Matrix Inequality (LMI). Clearly, if S is SDP representable, then S must be convex and semialgebraic (it is describable by conjunctions and disjunctions of polynomial equalities or(More)
This paper discusses the global minimization of rational functions with or without constraints. The sum of squares (SOS) relaxations are proposed to find the global minimum and minimizers. Some special features of the SOS relaxations are studied. As an application, we show how to find the nearest common divisors of polynomials via global minimization of(More)