Tuesday, May 21
8:00-10:00 AM
Esquinalt
MS20
Computational Mixed Integer Programming
The potential for solving large scale 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 interior point cutting plane algorithm; a branch and cut
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 branch and cut. 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 real world 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
MEM, 3/18/96