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 ideal is radical or the polynomial is...

Chvátal-Gomory (CG) cuts captures useful and efficient linear programs that the boundeddegree Lasserre/Sum-of-Squares (sos) hierarchy fails to capture. We present an augmentedversion of the sos hierarchy for 0/1 integer problems that implies the Bienstock-Zuckerberghierarchy by using high degree polynomials (when expressed in the standard monomial ba-sis). It follows that fo...

This paper proposes the control design of a nonlinear polynomial fuzzy system with ∞ H performance objective using a sum of squares (SOS) approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the ∞ H performance objective. The desi...

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, and discuss its various properties. W...

In this paper, we show that the controller synthesis of delayed systems can be formulated and solved in a convex manner through the use of a duality transformation, a structured class of operators, and the Sum-of-Squares (SOS) methodology. The contributions of this paper are as follows. We show that a dual stability condition can be formulated in terms of Lyapunov operators which are positive, ...

The notion of sos-convexity has recently been proposed as a tractable sufficient condition for convexity of polynomials based on sum of squares decomposition. A multivariate polynomial p(x) = p(x1, . . . , xn) is said to be sos-convex if its Hessian H(x) can be factored as H(x) = M (x) M (x) with a possibly nonsquare polynomial matrix M(x). It turns out that one can reduce the problem of decidi...

The problem of computing approximate GCDs of several polynomials with real or complex coefficients can be formulated as computing the minimal perturbation such that the perturbed polynomials have an exact GCD of given degree. We present algorithms based on SOS (Sum of Squares) relaxations for solving the involved polynomial or rational function optimization problems with or without constraints.

We propose a method for computing a perturbation bound that preserves the number of common zeros in (C×)n of a polynomial system (f1, . . . , fn), where C× = C \ {0} and fj ∈ C[x1, . . . , xn], by using Stetter’s results on the nearest polynomial with a given zero, Bernshtein’s theorem, and minimization techniques for rational functions such as sum of squares (SOS) relaxations.

The Majority is Stablest Theorem has numerous applications in hardness of approximation and social choice theory. We give a new proof of the Majority is Stablest Theorem by induction on the dimension of the discrete cube. Unlike the previous proof, it uses neither the ”invariance principle” nor Borell’s result in Gaussian space. The new proof is general enough to include all previous variants o...