GRASP and path-relinking: Recent advances and applications

A greedy randomized adaptive search procedure (GRASP) is a multi-start metaheuristic which applies local search to starting solutions generated by a greedy randomized construction procedure. Until recently, most implementations of GRASP assumed independence of its iterations, thus making no use of memory structures. Path-relinking is an intensification strategy which explores trajectories between elite solutions. Using … Read more

Multiprocessor Scheduling under Precedence Constraints: Polyhedral Results

We consider the problem of scheduling a set of tasks related by precedence constraints to a set of processors, so as to minimize their makespan. Each task has to be assigned to a unique processor and no preemption is allowed. A new integer programming formulation of the problem is given and strong valid inequalities are … Read more

A genetic algorithm for the phylogeny problem using an optimized crossover strategy based on path-relinking

A phylogenetic tree relates taxonomic units, based on their similarity over a set of characters. We propose a new genetic algorithm for the problem of building a phylogenetic tree under the parsimony criterion. This genetic algorithm makes use of an innovative optimized crossover strategy which is an extension of the path-relinking intensification technique originaly proposed … Read more

On-Line Scheduling to Minimize Average Completion Time Revisited

We consider the scheduling problem of minimizing the average weighted completion time on identical parallel machines when jobs are arriving over time. For both the preemptive and the nonpreemptive setting, we show that straightforward extensions of Smith’s ratio rule yield smaller competitive ratios than the previously best-known deterministic on-line algorithms. Citation Working Paper 4435-03, Sloan … Read more

Local Minima and Convergence in Low-Rank Semidefinite Programming

The low-rank semidefinite programming problem (LRSDP_r) is a restriction of the semidefinite programming problem (SDP) in which a bound r is imposed on the rank of X, and it is well known that LRSDP_r is equivalent to SDP if r is not too small. In this paper, we classify the local minima of LRSDP_r and … Read more

Sparsity in Sums of Squares of Polynomials

Representation of a given nonnegative multivariate polynomial in terms of a sum of squares of polynomials has become an essential subject in recent developments of sums of squares optimization and SDP (semidefinite programming) relaxation of polynomial optimization problems. We disscuss effective methods to obtain a simpler representation of a “sparse” polynomial as a sum of … Read more

Valid inequalities based on simple mixed-integer sets

In this paper we use facets of mixed-integer sets with two and three variables to derive valid inequalities for integer sets defined by a single equation. These inequalities also define facets of the master cyclic group polyhedron of Gomory. Facets of this polyhedron give strong valid inequalities for general mixed-integer sets, such as the well-known … Read more

Network Reinforcement

We give an algorithm for the following problem: given a graph $G=(V,E)$ with edge-weights and a nonnegative integer $k$, find a minimum cost set of edges that contains $k$ disjoint spanning trees. This also solves the following {\it reinforcement problem}: given a network, a number $k$ and a set of candidate edges, each of them … Read more

The Bundle Method in Combinatorial Optimization

We propose a dynamic version of the bundle method to get approximate solutions to semidefinite programs with a nearly arbitrary number of linear inequalities. Our approach is based on Lagrangian duality, where the inequalities are dualized, and only a basic set of constraints is maintained explicitly. This leads to function evaluations requiring to solve a … Read more

A hybrid multistart heuristic for the uncapacitated facility location problem

We present a multistart heuristic for the uncapacitated facility location problem, based on a very successful method we originally developed for the P-median problem. We show extensive empirical evidence to the effectiveness of our algorithm in practice. For most benchmarks instances in the literature, we obtain solutions that are either optimal or a fraction of … Read more