Narrow your search

Library

KU Leuven (3)

Odisee (3)

Thomas More Kempen (3)

Thomas More Mechelen (3)

UCLL (3)

ULiège (3)

VIVES (3)

ULB (2)

UCLouvain (1)

UGent (1)

More...

Resource type

book (3)


Language

English (3)


Year
From To Submit

2024 (1)

2020 (1)

2013 (1)

Listing 1 - 3 of 3
Sort by

Book
Distributed Optimization and Learning : A Control-Theoretic Perspective.
Authors: ---
ISBN: 9780443216374 0443216371 Year: 2024 Publisher: San Diego : Elsevier Science & Technology,

Loading...
Export citation

Choose an application

Bookmark

Abstract

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.


Book
Distributed Algorithms for Message-Passing Systems
Author:
ISBN: 3642381227 9783642381225 3642381235 Year: 2013 Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer,

Loading...
Export citation

Choose an application

Bookmark

Abstract

Distributed computing is at the heart of many applications. It arises as soon as one has to solve a problem in terms of entities -- such as processes, peers, processors, nodes, or agents -- that individually have only a partial knowledge of the many input parameters associated with the problem. In particular each entity cooperating towards the common goal cannot have an instantaneous knowledge of the current state of the other entities. Whereas parallel computing is mainly concerned with 'efficiency', and real-time computing is mainly concerned with 'on-time computing', distributed computing is mainly concerned with 'mastering uncertainty' created by issues such as the multiplicity of control flows, asynchronous communication, unstable behaviors, mobility, and dynamicity.   While some distributed algorithms consist of a few lines only, their behavior can be difficult to understand and their properties hard to state and prove. The aim of this book is to present in a comprehensive way the basic notions, concepts, and algorithms of distributed computing when the distributed entities cooperate by sending and receiving messages on top of an asynchronous network. The book is composed of seventeen chapters structured into six parts: distributed graph algorithms, in particular what makes them different from sequential or parallel algorithms; logical time and global states, the core of the book; mutual exclusion and resource allocation; high-level communication abstractions; distributed detection of properties; and distributed shared memory. The author establishes clear objectives per chapter and the content is supported throughout with illustrative examples, summaries, exercises, and annotated bibliographies.   This book constitutes an introduction to distributed computing and is suitable for advanced undergraduate students or graduate students in computer science and computer engineering, graduate students in mathematics interested in distributed computing, and practitioners and engineers involved in the design and implementation of distributed applications. The reader should have a basic knowledge of algorithms and operating systems.


Book
Distributed Optimization: Advances in Theories, Methods, and Applications
Authors: --- --- --- ---
ISBN: 9811561095 9811561087 Year: 2020 Publisher: Singapore : Springer Singapore : Imprint: Springer,

Loading...
Export citation

Choose an application

Bookmark

Abstract

This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.

Listing 1 - 3 of 3
Sort by