Annals of Operations Research

, Volume 22, Issue 1, pp 101–127 | Cite as

Parallel bundle-based decomposition for large-scale structured mathematical programming problems

  • Deepankar Medhi
Article

Abstract

In this paper, we present parallel bundle-based decomposition algorithms to solve a class of structured large-scale convex optimization problems. An example in this class of problems is the block-angular linear programming problem. By dualizing, we transform the original problem to an unconstrained nonsmooth concave optimization problem which is in turn solved by using a modified bundle method. Further, this dual problem consists of a collection of smaller independent subproblems which give rise to the parallel algorithms. We discuss the implementation on the CRYSTAL multi-computer. Finally, we present computational experience with block-angular linear programming problems and observe that more than 70% efficiency can be obtained using up to eleven processors for one group of test problems, and more than 60% efficiency can be obtained for relatively smaller problems using up to five processors for another group of problems.

Keywords

Programming Problem Test Problem Mathematical Programming Computational Experience Parallel Algorithm 

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Copyright information

© J.C. Baltzer AG, Scientific Publishing Company 1990

Authors and Affiliations

  • Deepankar Medhi
    • 1
  1. 1.Computer Science Telecommunications ProgramUniversity of Missouri-Kansas CityKansas CityUSA

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