Based on an extremely popular short course conducted by the authors for several Fortune 500 companies, this volume is designed to help professionals develop a deeper understanding of data networks and evolving integrated networks, and to explore today's various analysis and design tools. KEY TOPICS: It begins with an overview of the principles behind data networks, then develops an understanding of the modeling issues and mathematical analysis needed to compare the effectiveness of different networks. An ideal reference for Communication, Network, and Research and Development Engineers.

Data networks

Computers & Technology

Dimitri P. Bertsekas

Certification

Longman Higher Education

Based on an extremely popular short course conducted by the authors for several Fortune 500 companies, this volume is designed to help professionals develop a deeper understanding of data networks and evolving integrated networks, and to explore today's various analysis and design tools. KEY TOPICS: It begins with an overview of the principles behind data networks, then develops an understanding of the modeling issues and mathematical analysis needed to compare the effectiveness of different networks. An ideal reference for Communication, Network, and Research and Development Engineers.

Data Networks (2nd Edition)

Gallager

Pearson

This extensive rigorous texbook, developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and the interface between continuous and discrete optimization. Among its special features, the book: 1) provides extensive coverage of iterative optimization methods within a unifying framework 2) provides a detailed treatment of interior point methods for linear programming 3) covers in depth duality theory from both a variational and a geometrical/convex analysis point of view 4) includes much new material on a number of topics, such as neural network training, discrete-time optimal control, and large-scale optimization 5) includes a large number of examples and exercises detailed solutions of many of which are posted on the internet Much supplementary/support material can be found at the book's web page

Nonlinear Programming

Science & Math

Bertsekas, Dimitri P.

Mathematics

Athena Scientific

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

Parallel and Distributed Computation: Numerical Methods (Optimization and Neural Computation)

John Tsitsiklis

Tsitsiklis, John

An insightful, comprehensive, and up-to-date treatment of linear, nonlinear, and discrete/combinatorial network optimization problems, their applications, and their analytical and algorithmic methodology. It covers extensively theory, algorithms, and applications, and it aims to bridge the gap between linear and nonlinear network optimization on one hand, and integer/combinatorial network optimization on the other. Among its special features, the book: 1) provides a comprehensive account of the principal algorithms for linear network flow problems, including simplex, dual ascent, and auction algorithms 2) describes the application of network algorithms in many practical contexts, with special emphasis on data communication networks 3) develops in detail the computational complexity analysis of the main linear network optimization algorithms 4) covers extensively the main algorithms for specialized network problems, such as shortest path, max-flow, assignment, and traveling salesman 5) describes the main models for discrete network optimization problems, such as constrained shortest path, traveling salesman, vehicle routing, multidimensional assignment, facility location, spanning tree construction, etc 6) describes the main algorithmic approaches for integer-constrained network problems, such as branch-and-bound, Lagrangian relaxation and subgradient optimization, genetic algorithms, tabu search, simulated annealing, and rollout algorithms 7) develops the main methods for nonlinear network problems, such as convex separable and multicommodity flow problems arising in communication, transportation, and manufacturing contexts 8) discusses extensively auction algorithms, based on the author's original research on the subject 9) contains many examples, practical applications, illustrations, and exercises 10) contains much new material not found in any other textbook

Network Optimization: Continuous and Discrete Models (Optimization, Computation, and Control)

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.

Stochastic Optimal Control: The Discrete-Time Case (Optimization and Neural Computation Series)

Steven E. Shreve

Shreve, Steven E.

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 book: 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

Constrained Optimization and Lagrange Multiplier Methods (Optimization and neural computation series)

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. 
Among its special features, the book: 
(a) provides a unifying framework for sequential decision making 
(b) develops the theory of deterministic optimal control including the Pontryagin Minimum Principle 
(c) describes neuro-dynamic programming techniques for practical application of DP to complex problems that involve the dual curse of large dimension and lack of an accurate mathematical model 
(d) provides a comprehensive treatment of infinite horizon problems in the second volume, and an introductory treatment in the first volume 
(e) contains many exercises, with solutions of the most theoretical ones posted on the book's www page 
Highlights of the revision: (a) Much new material on suboptimal control, including neuro-dynamic programming and rollout algorithms, and their applications in combinatorial optimization and stochastic optimal control. (b) A section on estimation and control of systems with a non-probabilistic (set membership) description of uncertainty. (c) A section on infinite horizon continuous-time (semi-Markov) decision problems. (d) A new appendix dealing with the minimax and expected utility approaches for formulating decision problems under uncertainty.

Dynamic Programming and Optimal Control, Vol. 1 (Optimization and Computation Series)

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.

Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)

John N. Tsitsiklis

Tsitsiklis, John N.

Nonlinear programming

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