Author:
Acho L.
Affiliation:
Centro de Investigación y Desarrollo de Tecnología Digital del IPN (CITEDI-IPN), Mexico
Keyword(s):
Lagrangian Networks, Global Convergence, Convex Optimization, Lyapunov Theory.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Optimization Problems in Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
In this brief, a modification of Lagrangian networks given in (Xia Y., 2003) is presented. This modification improves the settling time of the convergence of Lagrangian networks to a stationary point; which is the optimal solution to the nonlinear convex programming problem with linear equality constraints. This is important because, in many real-time applications where Lagrangian networks are used to find an optimal solution, such as in signal and image processing, this settling time is interpreted as the processing time. Simulation results applied to a quadratic optimization problem show that settling time is improved from about to 2000 to 20 seconds. Lyapunov theory was used to obtain our main result.