Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Distributed line search via dynamic convex combinations
J. Cortés, S. Martínez
Proceedings of the IEEE Conference on Decision and
Control, Florence, Italy, 2013, pp. 2346-2351
Abstract
This paper considers multi-agent systems seeking to optimize a
convex aggregate function. We assume that the gradient of this
function is distributed, meaning that each agent can compute its
corresponding partial derivative with state information about its
neighbors and itself only. In such scenarios, the discrete-time
implementation of the gradient descent method poses the fundamental
challenge of determining appropriate agent stepsizes that guarantee
the monotonic evolution of the objective function. We provide a
distributed algorithmic solution to this problem based on the
aggregation of agent stepsizes via adaptive convex combinations.
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