Conic optimization for control, energy systems, and machine learning: Applications and algorithms

被引：6

作者：

Zhang, Richard Y. ^{[1
]}

Josz, Cedric ^{[2
]}

Sojoudi, Somayeh ^{[2
]}

机构：

[1] Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA

[2] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

来源：

ANNUAL REVIEWS IN CONTROL | 2019年 / 47卷

关键词：

Conic optimization; Numerical algorithms; Control theory; Energy; Machine learning; INTERIOR-POINT METHODS; OPTIMAL POWER-FLOW; INVERSE COVARIANCE ESTIMATION; SEMIDEFINITE PROGRAMS; MATRIX COMPLETION; NONNEGATIVE POLYNOMIALS; EXPLOITING SPARSITY; RANK SOLUTIONS; RELAXATIONS; COMPACT;

D O I：

10.1016/j.arcontrol.2018.11.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Optimization is at the core of control theory and appears in several areas of this field, such as optimal control, distributed control, system identification, robust control, state estimation, model predictive control and dynamic programming. The recent advances in various topics of modern optimization have also been revamping the area of machine learning. Motivated by the crucial role of optimization theory in the design, analysis, control and operation of real-world systems, this tutorial paper offers a detailed overview of some major advances in this area, namely conic optimization and its emerging applications. First, we discuss the importance of conic optimization in different areas. Then, we explain seminal results on the design of hierarchies of convex relaxations for a wide range of nonconvex problems. Finally, we study different numerical algorithms for large-scale conic optimization problems. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：323 / 340

页数：18

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