محاسبات موازی و توزیع‌شده: روش‌های عددی (بهینه‌سازی و محاسبات عصبی)

This highly acclaimed work, first published by Prentice Hall in 1989, is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the fundamental convergence, rate of convergence, communication, and synchronization issues associated with such algorithms. This is an extensive book, which aside from its focus on parallel and distributed algorithms, contains a wealth of material on a broad variety of computation and optimization topics. Among its special features, the book: 1) Quantifies the performance of parallel algorithms, including the limitations imposed by the communication and synchronization penalties. 2) Describes communication algorithms for a variety of system architectures including tree, mesh, and hypercube. 3) Provides a comprehensive convergence analysis of asynchronous methods and a comparison with their asynchronous counterparts. 4) Cove This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control. Neuro-dynamic programming uses neural network approximations to overcome the "curse of dimensionality" and the "curse of modeling" that have been the bottlenecks to the practical application of dynamic programming and stochastic control to complex problems. The methodology allows systems to learn about their behavior through simulation, and to improve their performance through iterative reinforcement. This book provides the first systematic presentation of the science and the art behind this exciting and far-reaching methodology. The book develops a comprehensive analysis of neuro-dynamic programming algorithms, and guides the reader to their successful application through case studies from complex problem areas. This widely referenced textbook, first published in 1982 by Academic Press, is the authoritative and comprehensive treatment of some of the most widely used constrained optimization methods, including the augmented Lagrangian/multiplier and sequential quadratic programming methods. Among its special features, the 1) treats extensively augmented Lagrangian methods, including an exhaustive analysis of the associated convergence and rate of convergence properties 2) develops comprehensively sequential quadratic programming and other Lagrangian methods 3) provides a detailed analysis of differentiable and nondifferentiable exact penalty methods 4) presents nondifferentiable and minimax optimization methods based on smoothing 5) contains much in depth research not found in any other textbook Neuro-dynamic programming, also known as reinforcement learning, is a recent methodology that can be used to solve very large and complex stochastic decision and control problems. It combines simulation, learning, neural networks or other approximation architectures, and the central ideas in dynamic programming. This book provides the first systematic presentation of the science and the art behind this promising methodology. It presents and unifies a large number of NDP methods, including several that are new; provides a rigorous development of the mathematical principles behind NDP; illustrates through case studies the practical application of NDP to complex problems and includes extensive background on dynamic programming and neural network training This is a substantially expanded (by about 30%) and improved edition of Vol. 1 of the best-selling dynamic programming book by Bertsekas. (A relatively minor revision of Vol.\ 2 is planned for the second half of 2001.) DP is a central algorithmic method for optimal control, sequential decision making under uncertainty, and combinatorial optimization. The treatment focuses on basic unifying themes and conceptual foundations. It illustrates the power of the method with many examples and applications from engineering, operations research, and economics. This book provides a unified, insightful, and modern treatment of linear optimization, that is, linear programming, network flow problems, and discrete optimization. It includes classical topics as well as the state of the art, in both theory and practice. This book develops in depths nonlinear programming, a central algorithmic method for optimization. The treatment focuses on constrained and unconstrained iterative algorithms, Lagrange multiplier theory, and large scale optimization methods. --back cover This research monograph is the authoritative and comprehensive treatment of the mathematical foundations of stochastic optimal control of discrete-time systems, including the treatment of the intricate measure-theoretic issues. Dimitri P. Bertsekas. Includes Bibliographical References (v. 1, P. 375-384; V. 2, P. 277-290) And Indexes. Dimitris Bertsimas, John N. Tsitsiklis. Includes Bibliographical References (p. 570-578) And Index.