4:00 PM-6:00 PM
Linear programming problems are pervasive in science, engineering, and industry. Modern mathematical models often lead to optimization problems with many thousands of variables and with stochastic or discrete characteristics. Moreover, these modeling and optimization activities often are part of a design process that requires rapid turnaround and repeated trials. In this context, effective and robust parallel methods are essential.
Despite the enormous advances in large-scale linear programming algorithms, relatively little attention has been given to the development of practical parallel approaches.
This minisymposium will present recent work in parallel algorithms and software for large-scale linear programming: continuous, stochastic, and discrete problems. Emphasis will be on performance and implementation issues, including the important role played by parallel sparse linear algebra.
Organizers: Thomas F. Coleman, Cornell University and Stephen J. Wright, Argonne National Laboratory
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