A hybrid steepest descent method for constrained convex optimization

被引：16

作者：

Gerard, Mathieu ^{[1
]}

De Schutter, Bart ^{[1
]}

Verhaegen, Michel ^{[1
]}

机构：

[1] Delft Univ Technol, Ctr Syst & Control, NL-2628 CD Delft, Netherlands

来源：

AUTOMATICA | 2009年 / 45卷 / 02期

关键词：

Real-time optimization; Convex optimization; Gradient methods; Steepest descent method; Hybrid systems; MODEL-PREDICTIVE CONTROL;

D O I：

10.1016/j.automatica.2008.08.018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes a hybrid steepest descent method to decrease over time any given convex cost function while keeping the optimization variables in any given convex set. The method takes advantage of the 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. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：525 / 531

页数：7

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