Exploiting Group Symmetry in Truss Topology Optimization

AMS classification: 90C22, 20Cxx, 70-08truss topology optimization;semidefinite programming;group symmetry

On Semidefinite Programming Relaxations of the Travelling Salesman Problem (Replaced by DP 2008-96)

AMS classification: 90C22, 20Cxx, 70-08traveling salesman problem;semidefinite programming;quadratic as- signment problem

On Semidefinite Programming Relaxations of Association Schemes With Application to Combinatorial Optimization Problems

AMS classification: 90C22, 20Cxx, 70-08traveling salesman problem;maximum bisection;semidefinite programming;association schemes

On the Lovasz O-number of Almost Regular Graphs With Application to Erdos-Renyi Graphs

AMS classifications: 05C69; 90C35; 90C22;Erdos-Renyi graph;stability number;Lovasz O-number;Schrijver O-number;C*-algebra;semidefinite programming

Exploiting Group Symmetry in Semidefinite Programming Relaxations of the Quadratic Assignment Problem

We consider semidefinite programming relaxations of the quadratic assignment problem, and show how to exploit group symmetry in the problem data. Thus we are able to compute the best known lower bounds for several instances of quadratic assignment problems from the problem library: [R.E. Burkard, S.E. Karisch, F. Rendl. QAPLIB — a quadratic assignment problem library. Journal on Global Optimization, 10: 291–403, 1997]. AMS classification: 90C22, 20Cxx, 70-08.quadratic assignment problem;semidefinite programming;group sym- metry

Optimal Embeddings of Distance Regular Graphs into Euclidean Spaces

In this paper we give a lower bound for the least distortion embedding of a distance regular graph into Euclidean space. We use the lower bound for finding the least distortion for Hamming graphs, Johnson graphs, and all strongly regular graphs. Our technique involves semidefinite programming and exploiting the algebra structure of the optimization problem so that the question of finding a lower bound of the least distortion is reduced to an analytic question about orthogonal polynomials.Comment: 10 pages, (v3) some corrections, accepted in Journal of Combinatorial Theory, Series

Approximations of convex bodies by polytopes and by projections of spectrahedra

We prove that for any compact set B in R^d and for any epsilon >0 there is a finite subset X of B of |X|=d^{O(1/epsilon^2)} points such that the maximum absolute value of any linear function ell: R^d --> R on X approximates the maximum absolute value of ell on B within a factor of epsilon sqrt{d}. We also discuss approximations of convex bodies by projections of spectrahedra, that is, by projections of sections of the cone of positive semidefinite matrices by affine subspaces.Comment: 13 pages, some improvements, acknowledgment adde