Algebra in Optimization - Minisymposium at ECM 2007

Tuesday afternoon, 15 July 2008

This ECM 2008 minisymposium focuses on the interplay between optimization and algebra, in particular, real algebraic geometry and invariant theory.

Real algebraic geometry has its roots in Hilbert's 17th problem and its solution by Artin in 1927, who proved that every nonnegative multivariate polynomial is a sum of squares of rational functions. Stengle, in his Positivstellensatz (1973), extended this result to a characterisation of positive polynomials on arbitrary semialgebraic sets, and more recently Schmüdgen and Putinar characterized positivity on compact semialgebraic sets using sums of squares of polynomials. While testing whether a polynomial is nonnegative is a hard problem, testing whether it is a sum of squares of polynomials can be formulated as a semidefinite program for which efficient algorithms exist. This observation opened the way to numerical algorithms, based on semidefinite optimization, for optimizing polynomials over semialgebraic sets. Fundamental problems include determining the complexity of sums of squares certificates of positivity, or characterizing which semialgebraic sets can be formulated as semidefinite programs. Such problems are addressed in the first session.

Semidefinite programming can also be used to obtain bounds on various combinatorial and geometric objects, like graph colorings, error correcting codes, or spherical codes and the famous kissing number. Such problems carry a large amount of symmetry, encoded in the action of a group. Representation theory of this group can be exploited to formulate tractable semidefinite programs. For instance, while the action of the orthogonal group on the space of polynomial functions on the unit sphere leads to Delsarte's well known linear programming bound on the kissing number, the action of the stabilizer of a point on the sphere leads to new, stronger semidefinite bounds. Understanding, exploiting and characterizing invariance properties of combinatorial structures is the focus of the second session.

Programme

13:25-14:10 Marie Françoise Roy (Rennes) Certificates of positivity in the Bernstein's basis

In the first part of the lecture I present results obtained recently with F. Boudaoud and F. Caruso in the univariate case: Let P∈ ℤ [X] be a polynomial of degree p with coefficients in the monomial basis of bit-size bounded by τ. If P is positive on [- 1, 1], we obtain a certificate of positivity using Bernstein's basis (i.e. a description of P making obvious that it is positive) of bit-size O (p4 (τ + log2 p)). Previous comparable results had a bit-size complexity exponential in p and τ.

In the second part I discuss multivariate certificates of positivity. New results of R. Leroy on the distance between control polygon and graph expressed in terms of relevant "second differences of the Bernstein coefficients" introduced in 1992 by Goodman and Peters will be presented. Finally the current state of the art on the size of certificates of positivity in the multivariate case will be described and open problems presented.

14:10-14:55 Markus Schweighofer (Rennes) Which sets can be described by linear matrix inequalities?

A linear matrix inequality (LMI) is an inequality of the form L(x):=A0+x1A1+...+xnAn≽ 0 where x=(x1,...,xn) is the vector of unknowns and the Ai are real symmetric matrices of equal size. A solution is a real vector x such that L(x) is a positive semidefinite matrix. We say that a set has a (lifted) LMI representation if it is (a projection of) the set of solutions of an LMI. The sets having a (lifted) LMI representation involving only diagonal matrices are exactly the closed convex polyhedrons. Like one can optimize a linear function over such polyhedra with linear optimization, one can optimize a linear function over sets having a (lifted) LMI representation with semidefinite optimization. Therefore it is an important question which sets have a (lifted) LMI representation and how such representions can actually be found. Using classical constructions of Jordan- and T-algebras due to Koecher and Vinberg, one can show that homogeneous closed convex cones always have an LMI representation. Vinnikov's determinantal representations of real plane curves have been used to solve the Lax conjecture and to show that three-dimensional hyperbolicity cones also have an LMI representation. It seems open if this remains true for higher dimensional hyperbolicity cones. In contrast to linear inequalities, LMIs become much more expressive if one allows lifted representations. In fact, the only condition known to be necessary for a set to have such a representation seems to be that it is convex and semialgebraic. Helton and Nie have recently shown that compact convex semialgebraic sets have a lifted LMI representation if they are given by polynomials satisfying a certain second order curvature condition. We will try to sketch some basic ideas of the proof of this important result on a very elementary level.

14:55-15:25 Break
15:25-16:10 Frank Vallentin (CWI Amsterdam) Semidefinite programming bounds

Two famous problems in geometry are the computation of the kissing number and the chromatic number in n-dimensional Euclidean space. The kissing number is the maximal number of unit spheres which can simultaneously touch a central unit sphere without pairwise overlapping. It is the starting point of discrete geometry when, in 1694, Newton and Gregory discussed if the solution in dimension 3 is 12 or 13. The chromatic number is the minimal number of colors one needs to paint all points in space so that pairs of points at distance 1 receive different colors. This problem was made popular by Erdős since the 1950s. The number in the plane is only known to lie between 4 and 7. By restricting to the case where sets of points having the same color are measurable the best known lower bound is 5.

In this talk I present a unified approach to find upper bounds for the kissing number and lower bounds for the measurable chromatic number based on a combination of semidefinite optimization and harmonic analysis. For the kissing number we found, with computer assistance, new upper bounds in various dimensions. For the measurable chromatic number we solved the optimization problem analytically and found new lower bounds in various dimensions and a new proof that it grows exponentially with the dimension.

Based on joint works with Christine Bachoc, Gabriele Nebe, Fernando Mario de Oliveira Filho.

16:10-16:55 Harm Derksen (Ann Arbor) G-invariant tensors

I present a generalization of a theorem of Schrijver which characterizes spaces of tensors which are invariant under a compact Lie group.

Organizers:

Jan Draisma and Monique Laurent.

Sponsors:

DIAMANT