Parallelization algorithms for modeling ARM processes
Abstract
AutoRegressive Modular (ARM) processes are a new class of nonlinear stochastic processes, which can accurately model a large class of stochastic processes, by capturing the empirical distribution and autocorrelation function simultaneously. Given an empirical sample path, the ARM modeling procedure consists of two steps: a global search for locating the minima of a nonlinear objective function over a large parametric space, and a local optimization of optimal or near optimal models found in the first step. In particular, since the first task calls for the evaluation of the objective function at each vector of the search space, the global search is a time consuming procedure. To speed up the computations, parallelization of the global search can be effectively used by partitioning the search space among multiple processors, since the requisite communication overhead is negligible.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2000
- Source ID
- 10.1155/s1048953300000332
Entities
People
- Benjamin Melamed
- Santokh Singh
Organizations
- Defense Advanced Research Projects Agency
- Rutgers University