Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Distributed Optimization with Applications to Sensor Networks and Machine Learning
Author: Distributed Optimization: Advances in Theories, Methods, and Applications
Author: Huaqing LiPublisher: Springer Nature
ISBN: 9811561095
Category : Technology & Engineering
Languages : en
Pages : 243
Book Description
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.
Distributed Sensing and Intelligent Systems
Author: Mohamed ElhosenyPublisher: Springer Nature
ISBN: 3030642585
Category : Technology & Engineering
Languages : en
Pages : 841
Book Description
This book is the proceeding of the 1st International Conference on Distributed Sensing and Intelligent Systems (ICDSIS2020) which will be held in The National School of Applied Sciences of Agadir, Ibn Zohr University, Agadir, Morocco on February 01-03, 2020. ICDSIS2020 is co-organized by Computer Vision and Intelligent Systems Lab, University of North Texas, USA as a scientific collaboration event with The National School of Applied Sciences of Agadir, Ibn Zohr University. ICDSIS2020 aims to foster students, researchers, academicians and industry persons in the field of Computer and Information Science, Intelligent Systems, and Electronics and Communication Engineering in general. The volume collects contributions from leading experts around the globe with the latest insights on emerging topics, and includes reviews, surveys, and research chapters covering all aspects of distributed sensing and intelligent systems. The volume is divided into 5 key sections: Distributed Sensing Applications; Intelligent Systems; Advanced theories and algorithms in machine learning and data mining; Artificial intelligence and optimization, and application to Internet of Things (IoT); and Cybersecurity and Secure Distributed Systems. This conference proceeding is an academic book which can be read by students, analysts, policymakers, and regulators interested in Distributed Sensing, Smart Network approaches, Smart Cities, IoT Applications, and Intelligent Applications. It is written in plain and easy language, and describes new concepts when they appear first so that a reader without prior background of the field finds it readable. The book is primarily intended for research students in sensor networks and IoT applications (including intelligent information systems, and smart sensors applications), academics in higher education institutions including universities and vocational colleges, policy makers and legislators.
Learning Over and for Networks with Efficiency and Security Guarantees
Author: Zai ShiPublisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 0
Book Description
New development of learning and networking makes these two areas more and more related to each other. First, due to the popularity of deep neural networks, learning with distributed dataset has become a common practice and new algorithms, especially optimization methods over networks, are needed with better efficiency and security. Second, new learning techniques like Bayesian optimization can help us solve networking problems with improved performance. In this thesis we will present our new results of techniques for both learning \textit{over} networks and learning \textit{for} networks. In the first part of the thesis, we focus on distributed optimization over a processing network consisting of $n$ processors, where each processor can only get access to its own function $f_i$ and communicate with its neighbors. The objective of the processors is to find $x^*$ that can minimize $\frac{1}{n}\sum_{i=1}^nf_i(x)$. This setup can be found in applications such as machine learning over ad-hoc sensor networks, where $f_i$ is the loss function based on the data collected by Sensor $i$. For this problem setup, we propose two methods for better efficiency.
Distributed Optimization and Learning
Author: Zhongguo LiPublisher: Elsevier
ISBN: 0443216371
Category : Technology & Engineering
Languages : en
Pages : 288
Book Description
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes. Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation Presents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniques Offers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches
Distributed Optimization with Application to Power Systems and Control
Author: Engelmann, AlexanderPublisher: KIT Scientific Publishing
ISBN: 3731511800
Category : Technology & Engineering
Languages : en
Pages : 226
Book Description
Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization.
An Integrated Algorithm for Distributed Optimization in Networked Systems
Author: Yapeng LuPublisher: Open Dissertation Press
ISBN: 9781374706682
Category :
Languages : en
Pages :
Book Description
This dissertation, "An Integrated Algorithm for Distributed Optimization in Networked Systems" by Yapeng, Lu, 呂亞鵬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4322423 Subjects: Business logistics - Data processing Wireless sensor networks Distributed artificial intelligence - Industrial applications Algorithms
Optimization, Learning, and Control for Interdependent Complex Networks
Author: M. Hadi AminiPublisher: Springer Nature
ISBN: 3030340945
Category : Technology & Engineering
Languages : en
Pages : 306
Book Description
This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
Generalized Distributed Learning Under Uncertainty for Camera Networks
Author: Sejong YoonPublisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 119
Book Description
Consensus-based distributed learning is a machine learning technique used to find the general consensus of local learning models to achieve a global objective. It is an important problem with increasing level of interest due to its applications in sensor networks. There are many benefits of distributed learning over traditional centralized learning, such as faster computation and reduced communication cost. In this dissertation, we focus on the merit that distributed learning can be performed in a fully decentralized way, which makes it one step further different from parallel computing approaches. First, we propose a general distributed probabilistic learning framework based on distributed optimization using an Alternating Direction Method of Multipliers (ADMM). We show that it can be applied to computer vision algorithms which have traditionally assumed a centralized computational setting. We demonstrate that our probabilistic interpretation of the decentralized processing is useful in dealing with missing values which are not explicitly handled in prior works. We provide empirical evaluations on a computer vision problem termed distributed affine structure from motion (SfM). Second, we propose two useful extensions of the distributed probabilistic learning framework. We first extend our framework so that it can incrementally update the learned model in an online fashion. To do this, we propose to use a Bayesian inference model based on Bregman ADMM (B-ADMM). Next, we show that the distributed learning tasks can be carried out more rapidly by introducing smart update strategies to the underlying ADMM optimization algorithm. By adaptively balancing primal and dual residuals of ADMM, we demonstrate an improved empirical convergence speed in a fully decentralized setting, without limiting the application range of ADMM-based optimization. Finally, we introduce a potential application of consensus-based distributed optimization on the human trajectory estimation problem. We formulate the trajectory estimation problem as a global optimization issue with constraints encoding various prior conditions that can be either allowed or forbidden in real world situations. We show that our method can effectively estimate the noisy, corrupted trajectories from off-the-shelf human trackers that could assist in human crowd analysis and simulation.
Distributed Optimization in Networked Systems
Author: Qingguo LüPublisher: Springer Nature
ISBN: 9811985596
Category : Computers
Languages : en
Pages : 282
Book Description
This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.