Semidefinite Programming Based Approaches to Home-away Assignment Problems in Sports Scheduling

For a given schedule of a round-robin tournament and a matrix of distances between homes of teams, an optimal home-away assignment problem is to find a home-away assignment that minimizes the total traveling distance. We propose a technique to transform the problem to MIN RES CUT. We apply Goemans and Williamson’s 0.878-approximation algorithm for MAX … Read more

Solving Maximum-Entropy Sampling Problems Using Factored Masks

We present a practical approach to Anstreicher and Lee’s masked spectral bound for maximum-entropy sampling, and we describe favorable results that we have obtained with a Branch-&-Bound algorithm based on our approach. By representing masks in factored form, we are able to easily satisfy a semidefiniteness constraint. Moreover, this representation allows us to restrict the … Read more

Semidefinite programming relaxations for graph coloring and maximal clique problems

The semidefinite programming formulation of the Lovasz theta number does not only give one of the best polynomial simultaneous bounds on the chromatic number and the clique number of a graph, but also leads to heuristics for graph coloring and extracting large cliques. This semidefinite programming formulation can be tightened toward either number by adding … Read more

Two-Stage Robust Network Flow and Design under Demand Uncertainty

We describe a two-stage robust optimization approach for solving network flow and design problems with demand uncertainty. We give an explicit characterization of the first-stage decisions and prove that the corresponding separation problem is NP-hard even for a network flow problem on a bipartite graph. We show, however, that if the second-stage network topology is … Read more

Finding optimal realignments in sports leagues using a branch-and-cut-and-price approach

The sports team realignment problem can be modelled as $k$-way equipartition: given a complete graph $K_{n}=(V,E)$, with edge weight $c_{e}$ on each edge, partition the vertices $V$ into $k$ divisions that have exactly $S$ vertices, so as to minimize the total weight of the edges that have both endpoints in the same division. In this … Read more

Strengthened Semidefinite Bounds for Codes

We give a hierarchy of semidefinite upper bounds for the maximum size $A(n,d)$ of a binary code of word length $n$ and minimum distance at least $d$. At any fixed stage in the hierarchy, the bound can be computed (to an arbitrary precision) in time polynomial in $n$; this is based on a result of … Read more

A PTAS for the minimization of polynomials of fixed degree over the simplex

We consider the problem of computing the minimum value $p_{\min}$ taken by a polynomial $p(x)$ of degree $d$ over the standard simplex $\Delta$. This is an NP-hard problem already for degree $d=2$. For any integer $k\ge 1$, by minimizing $p(x)$ over the set of rational points in $\Delta$ with denominator $k$, one obtains a hierarchy … Read more

Short communication: a larger clique for a DIMACS test

In the DIMACS benchmark suite for the maximum clique problem, the best known solution for test C2000.9 is a 78 nodes clique; optimality is not proved. We present a 79 nodes clique emerged during the testing of a heuristic algorithm. Article Download View Short communication: a larger clique for a DIMACS test

A branch and cut algorithm for solving the linear and quadratic integer programming problems

This paper first presents an improve cutting plane method for solving the linear programming problems, based on the primal simplex method with the current equivalent facet technique, in which the increment of objection function is allowed as a pivot variable to decide the search step size. We obtain a strong valid inequality from the objective … Read more

GRASP with path-relinking for the weighted maximum satisfiability problem

A GRASP with path-relinking for finding good-quality solutions of the weighted maximum satisfiability problem (MAX-SAT) is described in this paper. GRASP, or Greedy Randomized Adaptive Search Procedure, is a randomized multi-start metaheuristic, where at each iteration locally optimal solutions are constructed, each independent of the others. Previous experimental results indicate its effectiveness for solving weighted … Read more