Distributed Optimization and Learning : A Control-Theoretic Perspective.

Format:

Book

Author/Creator:

Li, Zhongguo.

Contributor:

Ding, Zhengtao.

Language:

English

Subjects (All):

Multiagent systems.

Distributed algorithms.

Physical Description:

1 online resource (288 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2024.

Summary:

This book explores the field of distributed optimization and learning, focusing on multi-agent systems and their applications in various domains such as robotics, autonomous vehicles, and smart grids. It delves into fundamental concepts like consensus control, cooperative and competitive optimization, and game theory. The authors aim to provide a comprehensive understanding of distributed algorithms and their convergence, as well as their practical applications in networked systems. The book is intended for researchers and practitioners in control systems, electrical engineering, and related fields, offering insights into both theoretical foundations and real-world implementations. Generated by AI.

Contents:

Front Cover

Distributed Optimization and Learning

Copyright

Contents

Biography

Preface

I Fundamental concepts and algorithms

1 Introduction to distributed optimization and learning

1.1 Background

1.2 Literature review

1.2.1 Consensus control

1.2.2 Cooperative optimization

1.2.3 Competitive games

1.3 Book organization

2 Control perspective to single agent optimization

2.1 Unconstrained optimization

2.1.1 Convex function and convex optimization

2.1.2 Gradient descent algorithm

2.1.3 Convergence analysis

2.2 Set constrained optimization

2.3 Optimization with affine constraints

2.3.1 Algorithm and convergence analysis for affine constrained optimization

2.4 Discrete-time control perspective to optimization

2.4.1 Exact gradient descent algorithm and convergence

2.4.2 Stochastic optimization with approximated gradient

3 Distributed frameworks: Graphs, consensus, optimization, and learning

3.1 Centralized networks: Structures and limitations

3.2 Centralized optimization and learning

3.3 Graph theory

3.3.1 Laplacian matrix Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9780443216374

0443216371

OCLC:

1450107622