Proceedings of the First Conference on Hypercube Multiprocessors on Hypercube multiprocessors
Proceedings of the First Conference on Hypercube Multiprocessors on Hypercube multiprocessors
Numerical Methods of Mathematical Optimization with ALGOL and FORTRAN Programs
Numerical Methods of Mathematical Optimization with ALGOL and FORTRAN Programs
What have we learnt from using real parallel machines to solve real problems?
C3P Proceedings of the third conference on Hypercube concurrent computers and applications - Volume 2
A hardware-based performance monitor for the Intel iPSC/2 hypercube
ICS '90 Proceedings of the 4th international conference on Supercomputing
A parallel formulation of interior point algorithms
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Parallel algorithm for solving linear programming problem under conditions of incomplete data
Automation and Remote Control
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Large, sparse, linear systems of equations arise frequently when constructing mathematical models of natural phenomena. Most often, these linear systems are fully constrained and can be solved via direct or iterative techniques. However, one important problem class requires solutions to underconstrained linear systems that maximize some objective function. These linear optimization problems are natural formulations of many business plans and often contain hundreds of equations with thousands of variables. Historically, linear optimization problems have been solved via the simplex method. Despite the excellent performance of the simplex method, the size of the optimization problems and the frequency of their solution make linear optimization a computationally taxing endeavor. This paper examines the performance of parallel variants of the simplex algorithm on the Intel iPSC, a message-based parallel system. Linear optimization test data are drawn from commercial sources and represent realistic problems. Analysis shows that the speedup obtained is sensitive to both the structure of the underlying data and the data partitioning.