Abstract. When a column generation approach is applied to decomposable mixed integer programming problems, it is standard to formulate and solve the master problem as a linear program. Seen in the dual space, this results in the algorithm known in the nonlinear programming community as the cutting-plane algorithm of Kelley and Cheney-Goldstein. However, more stable methods with better theoretical convergence rates are known and have been used as alternatives to this standard. One of them is the bundle method; our aim is to illustrate its differences with Kelley's method. In the process we review alternative stabilization techniques used in column generation, comparing them from both primal and dual points of view. Numerical comparisons are presented for five applications: cutting stock (which includes bin packing), vertex coloring, capacitated vehicle routing, multi-item lot sizing, and traveling salesman. We also give a sketchy comparison with the volume algorithm.

In some industries, a certain part can be needed in a very large number of different configurations. This is the case, e.g., for the electrical wirings in European car factories. A given configuration can be replaced by a more complete, therefore more expensive, one. The diversity management problem consists of choosing an optimal set of some given number k of configurations that will be produced, any nonproduced configuration being replaced by the cheapest produced one that is compatible with it. We model the problem as an integer linear program. Our aim is to solve those problems to optimality. The large-scale instances we are interested in lead to difficult LP relaxations, which seem to be intractable by the best direct methods currently available. Most of this paper deals with the use of Lagrangean relaxation to reduce the size of the problem in order to be able, subsequently, to solve it to optimality via classical integer optimization. Subject classifications: large-scale integer programming and relaxations: Lagrangean relaxation of a large linear integer program arising from an application; production planning: choice of production. Area of review: Optimization.

DIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. It is designed for either numerical or categorical data. Like the Ward agglomerative hierarchical clustering algorithm and the k-means partitioning algorithm, it is based on the minimization of the inertia criterion. However, unlike Ward and k-means, it provides a simple and natural interpretation of the clusters. The price paid by construction in terms of inertia by DIVCLUS-T for this additional interpretation is studied by applying the three algorithms on six databases from the UCI Machine Learning repository.

No abstract