Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Distributed line search for multi-agent convex optimization
J. Cortés, S. Martínez
Mathematical Control Theory
I. Nonlinear and Hybrid Control Systems, dedicated to the 60th
birthday of Arjan van der Schaft, ed. M. K. Camlibel, A. Julius,
R. Pasumarthy, and J. M. A. Scherpen, Lecture Notes in Control and
Information Sciences, vol. 461, Springer-Verlag, 2015, pp. 95-110
Abstract
This note 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 information about its neighbors and itself
only. In such scenarios, the discrete-time implementation of the
gradient descent method poses the basic 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.
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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
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