Tuesday, May 21
8:00-10:00 AM

Computational Mixed Integer Programming

The potential for solving largescale applied problems has always been a driving force behind the study of combinatorial optimization. In this minisymposium, several computational methodologies for solving important applications of mixed integer programming will be described. The applications addressed include airline fleet scheduling, survivability of telecommunication networks, and machine learning and statistical classification. The methodologies utilized include an interiorpoint cutting plane algorithm; a branchandcut algorithm incorporating heuristics, preprocessing, and approximation of nonlinear constraints; a cutting plane algorithm based on the concept of analytic center; and a column generation approach coupled with branchandcut. The presentations demonstrate the diversity of methods utilized to solve various classes of problems, and emphasize the increasing integration of nonlinear and discrete techniques within a common framework to solve realworld problems.

Organizer: Eva K. Lee,
Columbia University

Using an Interior Point Algorithm in a Cutting Plane Method for Solving Integer Programming Problems
John E. Mitchell, Rensselaer Polytechnic Institute
Linear and Nonlinear Mixed Integer Models for Machine Learning and Statistical Classification
Richard J. Gallagher, Columbia- Presbyterian Medical Center; Eva K. Lee, Organizer; and Dave Patterson, University of Montana
Survivability in Telecommunication Network
Robert Sarkissian, Universite de Geneve, Switzerland
Solving Fleet Scheduling Problems using Column Generation Techniques
Karla L. Hoffman and Peter Ball, George Mason University

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MEM, 3/18/96