In this paper, we survey the most recent methods that have been developed for the solution of semidefinite programs. We first concentrate on the methods that have been primarily motivated by the interior point (IP) algorithms for linear programming, putting special emphasis in the class of primal-dual path-following algorithms. We also survey methods that have … Read more
PHoM is a software package in C++ for finding all isolated solutions of polynomial systems using a polyhedral homotopy continuation method. Among three modules constituting the package, the first module StartSystem constructs a family of polyhedral-linear homotopy functions, based on the polyhedral homotopy theory, from input data for a given system of polynomial equations $\f(\x) … Read more
A single facility has to be located in competition with fixed existing facilities of similar type. Demand is supposed to be concentrated at a finite number of points, and consumers patronise the facility to which they are attracted most. Attraction is expressed by some function of the quality of the facility and its distance to … Read more
Based on the recent approach of Bertsimas and Sim \cite{bs1, bs2} to robust optimization in the presence of data uncertainty, we prove a bound on the probability that the robust solution gives an objective function value worse than the robust objective function value, under the assumption that only cost coefficients are subject to uncertainty. A … Read more
We develop a simple and yet very efficient exact algorithm for the problem of locating $p$ facilities and assigning clients to them in order to minimize the maximum distance between a client and the facility to which it is assigned. The algorithm iteratively sets a maximum distance value within which it tries to assign all … Read more
We introduce an algorithm to minimize a function of several variables with no convexity nor smoothness assumptions. The main peculiarity of our approach is the use of an the objective function model which is the difference of two piecewise affine convex functions. Bundling and trust region concepts are embedded into the algorithm. Convergence of the … Read more
We study the lift-and-project procedures for solving combinatorial optimization problems, as described by Lov\’asz and Schrijver, in the context of the stable set problem on graphs. We investigate how the procedures’ performances change as we apply fundamental graph operations. We show that the odd subdivision of an edge and the subdivision of a star operations … Read more
Taking a small graph, on which the randomized New-Best-In maximum clique heuristic fails to find the maximum clique, we construct on its basis a class of graphs exemplifying the inefficiency of SM greedy heuristics considered by Brockington and Culberson. We show that a 7(k+1)-vertex graph from this class is enough to provide a counterexample for … Read more
We survey how semidefinite programming can be used for finding good approximative solutions to hard combinatorial optimization problems. CitationPreliminary version appeared as Report PNA-R0210, CWI, Amsterdam, April 2002. To appear as Chapter in the Handbook on Discrete Optimization, K. Aardal, G. Nemhauser, R. Weismantel, eds., Elsevier Publishers.ArticleDownload View PDF
For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods solve a sequence of subproblems of the form “minimize an augmented Lagrangian function subject to linearized constraints”. Such methods converge rapidly near a solution but may not be reliable from arbitrary starting points. The well known software package MINOS has proven effective on many … Read more