Reference
M. Gerard, B. De Schutter, and M. Verhaegen, "A hybrid steepest descent method
for constrained convex optimization,"
Automatica, vol.
45, no. 2, pp. 525-531, Feb. 2009.
Abstract
This paper describes a hybrid steepest descent method to decrease over time any
given convex cost function while keeping the optimization variables into any
given convex set. The method takes advantage of properties of hybrid systems to
avoid the computation of projections or of a dual optimum. The convergence to a
global optimum is analyzed using Lyapunov stability arguments. A discretized
implementation and simulation results are presented and analyzed. This method
is of practical interest to integrate real-time convex optimization into
embedded controllers thanks to its implementation as a dynamical system, its
simplicity, and its low computation cost.
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BibTeX
@article{GerDeS:08-015,
author = {Gerard, Mathieu and De Schutter, Bart and Verhaegen, Michel},
title = {A Hybrid Steepest Descent Method for Constrained Convex
Optimization},
journal = {Automatica},
volume = {45},
number = {2},
pages = {525--531},
month = feb,
year = {2009}
}