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

Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi

Research output: Contribution to journalReview article

Abstract

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.

Original languageEnglish (US)
Pages (from-to)323-340
Number of pages18
JournalAnnual Reviews in Control
Volume47
DOIs
StatePublished - 2019
Externally publishedYes

Fingerprint

Power control
Learning systems
Distributed parameter control systems
Model predictive control
State estimation
Robust control
Control theory
Dynamic programming
Identification (control systems)

Keywords

  • Conic optimization
  • Control theory
  • Energy
  • Machine learning
  • Numerical algorithms

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

Cite this

Conic optimization for control, energy systems, and machine learning : Applications and algorithms. / Zhang, Richard Y.; Josz, Cédric; Sojoudi, Somayeh.

In: Annual Reviews in Control, Vol. 47, 2019, p. 323-340.

Research output: Contribution to journalReview article

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