Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Noise-to-state stable distributed convex optimization on
weight-balanced digraphs
D. Mateos-Nuñez, J. Cortés
Proceedings of the IEEE Conference on Decision and
Control, Florence, Italy, 2013, pp. 2781-2786
Abstract
This paper studies the robustness under additive persistent noise of
a class of continuous-time distributed algorithms for convex
optimization. A group of agents, each with its own private
objective function and communicating over a weight-balanced digraph,
seeks to determine the global decision vector that minimizes the sum
of all the functions. Under mild conditions on the local objective
functions, we establish that the distributed algorithm is
noise-to-state exponentially stable in second moment with respect to
the optimal solution. Our technical approach combines notions and
tools from graph theory, stochastic differential equations, and
Lyapunov stability analysis. Simulations illustrate our results.
pdf   |
  ps.gz
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo