Complexity estimates for the Schmu¨dgen Positivstellensatz
Journal of Complexity
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
On the complexity of Schmüdgen's positivstellensatz
Journal of Complexity
Sparsity in sums of squares of polynomials
Mathematical Programming: Series A and B
Optimization of Polynomials on Compact Semialgebraic Sets
SIAM Journal on Optimization
Semidefinite Approximations for Global Unconstrained Polynomial Optimization
SIAM Journal on Optimization
Minimizing Polynomials via Sum of Squares over the Gradient Ideal
Mathematical Programming: Series A and B
Global minimization of rational functions and the nearest GCDs
Journal of Global Optimization
Proceedings of the twenty-first international symposium on Symbolic and algebraic computation
Improved Approximation Guarantees through Higher Levels of SDP Hierarchies
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Partitioning procedure for polynomial optimization
Journal of Global Optimization
Biquadratic Optimization Over Unit Spheres and Semidefinite Programming Relaxations
SIAM Journal on Optimization
Journal of Symbolic Computation
SIAM Journal on Optimization
Welfare-maximizing correlated equilibria using Kantorovich polynomials with sparsity
Journal of Global Optimization
A Semidefinite Programming approach for solving Multiobjective Linear Programming
Journal of Global Optimization
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Let S={x@?R^n|g"1(x)=0,...,g"m(x)=0} be a basic closed semialgebraic set defined by real polynomials g"i. Putinar's Positivstellensatz says that, under a certain condition stronger than compactness of S, every real polynomial f positive on S possesses a representation f=@?"i"="0^m@s"ig"i where g"0@?1 and each @s"i is a sum of squares of polynomials. Such a representation is a certificate for the nonnegativity of f on S. We give a bound on the degrees of the terms @s"ig"i in this representation which depends on the description of S, the degree of f and a measure of how close f is to having a zero on S. As a consequence, we get information about the convergence rate of Lasserre's procedure for optimization of a polynomial subject to polynomial constraints.