Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4628372 | Applied Mathematics and Computation | 2014 | 14 Pages |
Abstract
In this paper, we consider nonconvex differentiable programming under linear and nonlinear differentiable constraints. A reduced gradient and GRG (generalized reduced gradient) descent methods involving stochastic perturbation are proposed and we give a mathematical result establishing the convergence to a global minimizer. Numerical examples are given in order to show that the method is effective to calculate. Namely, we consider classical tests such as the statistical problem, the octagon problem, the mixture problem and an application to the linear optimal control servomotor problem.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Abdelkrim El Mouatasim, Rachid Ellaia, Eduardo Souza de Cursi,