Optimization Over Time
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Optimization Over Time
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Author : Peter Whittle
language : en
Publisher:
Release Date : 1983
Optimization Over Time written by Peter Whittle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with categories.
Optimization Over Time
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Author : Peter Whittle
language : en
Publisher:
Release Date : 1982
Optimization Over Time written by Peter Whittle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with Dynamic programming categories.
Optimization Over Time
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Author : Peter Whittle
language : en
Publisher:
Release Date : 1983
Optimization Over Time written by Peter Whittle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Dynamic programming categories.
Optimization Over Time Dynamic Programming And Stochastic Control
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Author : Peter Whittle
language : en
Publisher:
Release Date : 1982
Optimization Over Time Dynamic Programming And Stochastic Control written by Peter Whittle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with Mathematics categories.
Convex Optimization
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Author : Stephen P. Boyd
language : en
Publisher: Cambridge University Press
Release Date : 2004-03-08
Convex Optimization written by Stephen P. Boyd and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-08 with Business & Economics categories.
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Distributed Optimization In Networked Systems
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Author : Qingguo Lü
language : en
Publisher: Springer Nature
Release Date : 2023-02-08
Distributed Optimization In Networked Systems written by Qingguo Lü and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-08 with Computers categories.
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.
Real Time Pde Constrained Optimization
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Author : Lorenz T. Biegler
language : en
Publisher: SIAM
Release Date : 2007-01-01
Real Time Pde Constrained Optimization written by Lorenz T. Biegler and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-01 with Mathematics categories.
Many engineering and scientific problems in design, control, and parameter estimation can be formulated as optimization problems that are governed by partial differential equations (PDEs). The complexities of the PDEs--and the requirement for rapid solution--pose significant difficulties. A particularly challenging class of PDE-constrained optimization problems is characterized by the need for real-time solution, i.e., in time scales that are sufficiently rapid to support simulation-based decision making. Real-Time PDE-Constrained Optimization, the first book devoted to real-time optimization for systems governed by PDEs, focuses on new formulations, methods, and algorithms needed to facilitate real-time, PDE-constrained optimization. In addition to presenting state-of-the-art algorithms and formulations, the text illustrates these algorithms with a diverse set of applications that includes problems in the areas of aerodynamics, biology, fluid dynamics, medicine, chemical processes, homeland security, and structural dynamics. Audience: readers who have expertise in simulation and are interested in incorporating optimization into their simulations, who have expertise in numerical optimization and are interested in adapting optimization methods to the class of infinite-dimensional simulation problems, or who have worked in "offline" optimization contexts and are interested in moving to "online" optimization.
Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems
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Author : Tatiana Tatarenko
language : en
Publisher: Springer
Release Date : 2017-09-19
Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems written by Tatiana Tatarenko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-19 with Science categories.
This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.
Lectures On Convex Optimization
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Author : Yurii Nesterov
language : en
Publisher: Springer
Release Date : 2018-11-19
Lectures On Convex Optimization written by Yurii Nesterov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-19 with Mathematics categories.
This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning. Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail. Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.
Decentralized Optimization In Networks
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Author : Qingguo Lü,
language : en
Publisher: Morgan Kaufmann
Release Date : 2025-08-01
Decentralized Optimization In Networks written by Qingguo Lü, and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-01 with Computers categories.
Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and solutions to decentralized optimization problems. The book demonstrates the application of decentralized optimization algorithms to enhance communication and computational efficiency, solve large-scale datasets, maintain privacy preservation, and address challenges in complex decentralized networks. The book covers key topics such as event-triggered communication, random link failures, zeroth-order gradients, variance-reduction, Polyak's projection, stochastic gradient, random sleep, and differential privacy. It also includes simulations and practical examples to illustrate the algorithms' effectiveness and applicability in real-world scenarios. - Introduces the latest and advanced algorithms in decentralized optimization of networked control systems - Proposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithms - Constructs the frameworks of convergence and complexity analysis, privacy, security proof, and performance evaluation - Includes systematic detailed implementations on how decentralized optimization algorithms solve the problems in real world systems: smart grid systems, online learning systems, wireless sensor systems, etc. - Helps readers develop their own novel, decentralized optimization algorithms