Parallel and serial variational inequality decomposition algorithms for multicommodity market equilibrium problems
The authors have applied parallel and serial variational inequality (VI) diagonal decomposition algorithms to large-scale multicommodity market equilibrium problems. These decomposition algorithms resolve the VI problems into single commodity problems, which are then solved as quadratic programming problems. The algorithms are implemented on an IBM 3090-600E, and randomly generated linear and nonlinear problems with as many as 100 markets and 12 commodities are solved. The computational results demonstrate that the parallel diagonal decomposition scheme is amenable to parallelization. This is the first time that multicommodity equilibrium problems of this scale and level of generality have been solved. Furthermore, this is the first study to compare the efficiencies of parallel and serial VI decomposition algorithms. Although the authors have selected as a prototype an equilibrium problem in economics, virtually any equilibrium problem can be formulated and studied as a variational inequality problem. Hence, their results are not limited to applications in economics and operations research.
- Research Organization:
- Univ. of Massachusetts, Amherst, MA (US)
- OSTI ID:
- 6089821
- Journal Information:
- Int. J. Supercomput. Appl.; (United States), Vol. 3:1
- Country of Publication:
- United States
- Language:
- English
Similar Records
Quadratic based primal-dual algorithms for multicommodity convex and linear cost transportation problems with serial and parallel implementations
The parallel decomposition of linear programs