Toward a scalable concurrent architecture for real-time processing of stochastic control and optimization problems

Abstract

Reports on the development of a scalable multiple-instruction multiple-data (MIMD) concurrent architecture which is intended to serve as an effective alternative for solving stochastic differential and optimization systems. This architecture has in turn motivated the application of group theory and invariance analysis to acquire further insights in understanding the original problem. The speed-up ratios attained by this architecture can realistically justify its potential deployment in certain real-time applications. A case study related to real-time stochastic control and optimization serve to illustrate this possibility.<>