Optimization Over Time
Author: Peter Whittle
Publisher:
Published: 1982
Total Pages: 338
ISBN-13:
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Distributed Optimization: Advances in Theories, Methods, and Applications
Author: Huaqing Li
Publisher: Springer Nature
Published: 2020-08-04
Total Pages: 257
ISBN-13: 9811561095
DOWNLOAD EBOOKThis 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.
Multi-agent Optimization
Author: Angelia Nedić
Publisher: Springer
Published: 2018-11-02
Total Pages: 310
ISBN-13: 9783319971414
DOWNLOAD EBOOKThis book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers.
Continuous-time Stochastic Control and Optimization with Financial Applications
Author: Huyên Pham
Publisher: Springer Science & Business Media
Published: 2009-05-28
Total Pages: 243
ISBN-13: 3540895000
DOWNLOAD EBOOKStochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.
Handbook of Formal Optimization
Author: Anand J. Kulkarni
Publisher: Springer Nature
Published:
Total Pages: 1406
ISBN-13: 9819738202
DOWNLOAD EBOOKIntroduction to Applied Linear Algebra
Author: Stephen Boyd
Publisher: Cambridge University Press
Published: 2018-06-07
Total Pages: 477
ISBN-13: 1316518965
DOWNLOAD EBOOKA groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Convex Optimization Algorithms
Author: Dimitri Bertsekas
Publisher: Athena Scientific
Published: 2015-02-01
Total Pages: 576
ISBN-13: 1886529280
DOWNLOAD EBOOKThis book provides a comprehensive and accessible presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. This is facilitated by the extensive use of analytical and algorithmic concepts of duality, which by nature lend themselves to geometrical interpretation. The book places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning. The book is aimed at students, researchers, and practitioners, roughly at the first year graduate level. It is similar in style to the author's 2009"Convex Optimization Theory" book, but can be read independently. The latter book focuses on convexity theory and optimization duality, while the present book focuses on algorithmic issues. The two books share notation, and together cover the entire finite-dimensional convex optimization methodology. To facilitate readability, the statements of definitions and results of the "theory book" are reproduced without proofs in Appendix B.
Algorithms for Convex Optimization
Author: Nisheeth K. Vishnoi
Publisher: Cambridge University Press
Published: 2021-10-07
Total Pages: 314
ISBN-13: 1108633994
DOWNLOAD EBOOKIn the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.
Distributed Optimization, Game and Learning Algorithms
Author: Huiwei Wang
Publisher: Springer Nature
Published: 2021-01-04
Total Pages: 227
ISBN-13: 9813345284
DOWNLOAD EBOOKThis book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
Optimization Theory with Applications
Author: Donald A. Pierre
Publisher: Courier Corporation
Published: 2012-07-12
Total Pages: 644
ISBN-13: 0486136957
DOWNLOAD EBOOKBroad-spectrum approach to important topic. Explores the classic theory of minima and maxima, classical calculus of variations, simplex technique and linear programming, optimality and dynamic programming, more. 1969 edition.
