Product Description
This collection of 235 problems is designed for undergraduates who have completed a year's course in mathematical programming.Each section of linear and non-linear problems begins with simple exercises and proceeds to more difficult ones.Solutions are based on first principles and can be found using a desk calculator. Answers to all problems are provided. ... Read more

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Stressing the use of several software packages based on simplex method variations, this text teaches linear programming's four phases through actual practice. It shows how to decide whether LP models should be applied, set up appropriate models, use software to solve them, and examine solutions to a ... Read more

Customer Reviews (1)

Way to go Dr. D!
This was one of the best books I've read. Even if you are just a high school principal, you are a tremendous writer and I hope you continue to strike fear into the hearts of readers everywhere! You rock Triple D! ... Read more

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A comprehensive, up-to-date text on linear programming. Covers all practical modeling, mathematical, geometrical, algorithmic, and computational aspects. Surveys recent developments in the field, including the Ellipsoid method. Includes extensive examples and exercises. Designed for advanced undergraduates or graduates majoring in engineering, mathematics, or business administration. ... Read more

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Linear Programming , Katta Murty
This is the best book in linear programming, rigurous, serius
and excitant. ... Read more

Product Description
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications.The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables.The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions.Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference. ... Read more

Customer Reviews (3)

the book by the originators of the methodology
Nelder and Wedderburn wrote the seminal paper on generalized linear models in the 1970s. Since then John Nelder has pioneered the research and software development of the methods. This is the first of several excellent texts on generalized linear models. It illustrates how through the use of a link function many classical statistical models can be unified into one general form of model. This unification is helpful both theoretically and computationally. Various applications are presented in a clear manner.

Very comprehensive, very helpful.
The first edition is already a well-known text and reference, this expanded version is even better. Very comprehensive and very helpful.

One of the best books on modelling
This is an important book. It is a mature, deep introduction to generalized linear models.

General linear models extend multiple linear models to include cases in which the distribution of the dependent variableis part of the exponential family and the expected value of the dependentvariable is a function of the linear predictor. Besides the normal(Gaussian) distribution, the binomial distribution, the Poissondistribution and the Gamma distribution, are just some of the exponentialfamily members most frequently encountered in the scientific literature.Using appropriate functions to join the dependent variable to the linearpredictor many classic models of applied statistics are included in thebroad frame of generalized linear models: "logistic regression",log-linear models, Cox's proportional hazards models are just some of them.

Further extensions to the "base" family of generalized linearmodels, such as those based on the use ofquasi-likelihood functions, andmodels in which both the expected value and the dispersion are function ofa linear predictor, are well presented in the book.

Examples, andexercises, introduce many non-banal, useful, designs.

There are someminor drawbacks. Some more advanced topics might have been introduced moresmoothly (i.e. conditional likelihood). Some other topics are betterunderstood when you are already familiar with the specific object of study(i.e. Cox's proportional hazards models as a generalized linear model). The book does not provide software examples, nor is it related with anyspecific statistical package. However, the maturity of the reader to whomthe book is addressed should be so high that translating the majority ofthe examples presented in the book in the "language" of afamiliar statistical package should not be a problem. ... Read more

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Awarded the Frederick W. Lanchester Prize in 1994 for its valuable contributions to operations research and the management sciences, this mathematically rigorous book remains the standard reference on the linear complementarity problem. Its comprehensive treatment of the computation of equilibria arising from engineering, economics, and finance, plus chapter-ending exercises and Notes and References sections make it equally useful for a graduate-level course or for self-study. For this new edition the authors have corrected typographical errors, revised difficult or faulty passages, and updated the bibliography.

Audience: This book is intended for researchers and graduate students in various fields including optimization, game theory, and finance, and diverse engineering disciplines, especially computer science and mechanical engineering.

Contents: Preface to the Classics Edition; Preface; Glossary of Notation; Numbering System; Chapter 1: Introduction; Chapter 2: Background; Chapter 3: Existence and Multiplicity; Chapter 4: Pivoting Methods; Chapter 5: Iterative Methods; Chapter 6: Geometry and Degree Theory; Chapter 7: Sensitivity and Stability Analysis; Bibliography; Index. ... Read more

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This book provides a self-contained exposition of the theory of linear models, including practical aspects of residuals and data analysis. ... Read more

Customer Reviews (1)

Still the best logic text for computer science
I've been using this book to teach introductory mathematical logic to computer science students for many years.Every year or two, I look again for something that might cover the ground better, but, despite somenotational flaws and an occasional error or poor choice of phrase, this onecomes out on top every time. ... Read more

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Helps Students Understand Mathematical Programming Principles and Solve Real-World Applications

Supplies enough mathematical rigor yet accessible enough for undergraduates
Integrating a hands-on learning approach, a strong linear algebra focus, Maple™ software, and real-world applications, Linear and Nonlinear Programming with Maple™: An Interactive, Applications-Based Approach introduces undergraduate students to the mathematical concepts and principles underlying linear and nonlinear programming. This text fills the gap between management science books lacking mathematical detail and rigor and graduate-level books on mathematical programming.

Essential linear algebra tools
Throughout the text, topics from a first linear algebra course, such as the invertible matrix theorem, linear independence, transpose properties, and eigenvalues, play a prominent role in the discussion. The book emphasizes partitioned matrices and uses them to describe the simplex algorithm in terms of matrix multiplication. This perspective leads to streamlined approaches for constructing the revised simplex method, developing duality theory, and approaching the process of sensitivity analysis. The book also discusses some intermediate linear algebra topics, including the spectral theorem and matrix norms.

Maple enhances conceptual understanding and helps tackle problems
Assuming no prior experience with Maple, the author provides a sufficient amount of instruction for students unfamiliar with the software. He also includes a summary of Maple commands as well as Maple worksheets in the text and online. By using Maple’s symbolic computing components, numeric capabilities, graphical versatility, and intuitive programming structures, students will acquire a deep conceptual understanding of major mathematical programming principles, along with the ability to solve moderately sized real-world applications.

Hands-on activities that engage students
Throughout the book, student understanding is evaluated through "waypoints" that involve basic computations or short questions. Some problems require paper-and-pencil calculations; others involve more lengthy calculations better suited for performing with Maple. Many sections contain exercises that are conceptual in nature and/or involve writing proofs. In addition, six substantial projects in one of the appendices enable students to solve challenging real-world problems.

Customer Reviews (1)

Clear, concise, and useful.
This book is a very well written tutorial in the use of Maple's impressive math programming tools, which provide a super convenient means of computing solutions to challenging optimization problems. It would be useful as both a text and a concise partial reference for beginning Maple users and/or math programmers.Book is a it pricey but worth it. ... Read more

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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. ... Read more

Customer Reviews (2)

One of my favorites
This was one of my favorite books as a student and it still is. The book presents much of the theory underlying reinforcement learning (the authors like to call it neuro-dynamic programming) in a clear and compact manner.While there are detailed mathematical proofs in many chapters, the book is actually pretty easy to read!It for sure helped me build an intuitive understanding of the mechanism of reinforcement learning when I was a student. The book is certaintly useful for beginners to this area; Ph.D. students are likely to get even more out of it.

Some special features of the book are:

* A clear discussion on the connection between classical dynamic programming and reinforcement learning (RL) along with the links to the Robbins-Monro algorithm
* Discussion on numerous types of approximate policy iteration
* A clear discussion on TD(lambda)
* Extensions to average reward (cost) problems
* A rigorous discussion of how function approximation works within this framework
* Treatment of the stochastic shortest path problem
* Detailed proofs of convergence of numerous NDP/RL algorithms
* Material/ideas on numerous topics on NDP/RL (for future research)

From the author of Approximate Dynamic Programming
Neuro-Dynamic Programming was, and is, a foundational reference for anyone wishing to work in the field that goes under names such as approximate dynamic programming, adaptive dynamic programming, reinforcement learning or, as a result of this book, neuro-dynamic programming.This is a clearly written treatment of the theory behind methods to solve dynamic programs by approximating the value function (in this book, the cost-to-go function).

The book is primarily for doctoral students and researchers.It provides descriptions of many solution strategies, but not at the level of detailed recipes.The presentation focuses on theory, but at a very readable level.Building on the prior work of the authors, this is the first book that brings together approximation methods in dynamic programming, with the theory of stochastic approximation methods (with its origins in Robbins and Monro) that provide the foundation for convergence proofs.

This book, along with Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) by Sutton and Barto, were major references when I started my own work in this field, leading up to my book: Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics) published by John Wiley and Sons.My students still use Neuro-Dynamic Programming as a reference for their research.

Warren Powell
Professor
Operations Research and Financial Engineering
Princeton University ... Read more

Product Description
Since the first edition of this book was published in 1996, tremendous progress has been made in the scientific and engineering disciplines regarding the use of iterative methods for linear systems. The size and complexity of the new generation of linear and nonlinear systems arising in typical applications has grown. Solving the three-dimensionalmodels of these problems using direct solvers is no longer effective. At the same time, parallel computing has penetrated these application areas as it became less expensive and standardized. Iterative methods are easier than direct solvers to implement on parallel computers but require approaches and solution algorithms that are different from classical methods.

Iterative Methods for Sparse Linear Systems, Second Edition gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations. These equations can number in the millions and are sparse in the sense that each involves only a small number of unknowns. The methods described are iterative, i.e., they provide sequences of approximations that will converge to the solution.

This new edition includes a wide range of the best methods available today. The author has added a new chapter on multigrid techniques and has updated material throughout the text, particularly the chapters on sparse matrices, Krylov subspace methods, preconditioning techniques, and parallel preconditioners. Material on older topics has been removed or shortened, numerous exercises have been added, and many typographical errors have been corrected.The updated and expanded bibliography now includes more recent works emphasizing new and important research topics in this field.

Audience
This book can be used to teach graduate-level courses on iterative methods for linear systems. Engineers and mathematicians will find its contents easily accessible, and practitioners and educators will value it as a helpful resource. The preface includes syllabi that can be used for either a semester- or quarter-length course in both mathematics and computer science.

Contents
Preface to the Second Edition; Preface to the First Edition; Chapter 1: Background in Linear Algebra; Chapter 2: Discretization of Partial Differential Equations; Chapter 3: Sparse Matrices; Chapter 4: Basic Iterative Methods; Chapter 5: Projection Methods; Chapter 6: Krylov Subspace Methods, Part I; Chapter 7: Krylov Subspace Methods, Part II; Chapter 8: Methods Related to the Normal Equations; Chapter 9: Preconditioned Iterations; Chapter 10: Preconditioning Techniques; Chapter 11: Parallel Implementations; Chapter 12: Parallel Preconditioners; Chapter 13: Multigrid Methods; Chapter 14: Domain Decomposition Methods; Bibliography; Index. ... Read more

Customer Reviews (3)

Excellent work
We used this book to prove a theorem in our studies that is directly related to my PhD thesis on spatial data mining and spatial statistics. This book is a master-piece.
Thanks Dr. Saad.

Wonderful Book
This is one of my favorite books in my library on this subject. Also I have used this book for my class as main textbook along with "Iterative Methods for Solving Linear and Nonlinear Equations" by C. T. Kelley , which is another SIAM book.
Highly recommended.

Great Book
This is a great book for this subject.The book is easy to follow and Saad does a wonderful job ofillustrating with examples.This is a great textbook or a book for reference.This book does a particularly good jobwith Krylov methods and does a reasonable job with preconditioning. ... Read more

Product Description
This text is concerned primarily with the theory of linear and nonlinear programming, and a number of closely-related problems, and with algorithms appropriate to those problems. In the first part of the book, the authors introduce the concept of duality which serves as a unifying concept throughout the book. The simplex algorithm is presented along with modifications and adaptations to problems with special structures. Two alternative algorithms, the ellipsoidal algorithm and Karmarker's algorithm, are also discussed, along with numerical considerations. the second part of the book looks at specific types of problems and methods for their solution. This book is designed as a textbook for mathematical programming courses, and each chapter contains numerous exercises and examples. ... Read more

Customer Reviews (2)

Karmarkar and back again
Don't read this book expecting computer programming instruction in the "linear" language. It's a whole different animal of operations research. Linear programming concerns minimizing and maximizing linearfunctions under a set of linear constraints. This book brings the bestproblems to linear programming solves and the recent theorems. I found it'shandling of the Karmarkar algorithm easier to follow than most other I'veseen. It's hard to find information this current outside of journals.

Karmarkar and back again
As an undergrad math major, I found this book incredibly helpful in understanding the Karmarker Algorithm or, more specifically, their twist on it. Some sections require many rereadings and the differences between theirexplanations and the original Karmarkar article are stark. But, overall,it's a clear a read as can be expected from an math/operations researchtext. ... Read more

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Bridging the gap between the theory of mathematical programming and the varied, real-world practices of industrial engineers, this state-of-the-art reference text introduces the latest developments in linear, integer, multiobjective, stochastic, network, and dynamic programming, and details many relevant industrial engineering applications. ... Read more

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This undergraduate textbook, which includes problems and exercises, covers all applications of linear algebra. Topics include constructing curves and surfaces through specified points, plane geometry, graphy theory, computer graphics, linear programming and much more. ... Read more

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In the pages of this text readers will find nothing less than a unified treatment of linear programming. Without sacrificing mathematical rigor, the main emphasis of the book is on models and applications. The most important classes of problems are surveyed and presented by means of mathematical formulations, followed by solution methods and a discussion of a variety of "what-if" scenarios. Non-simplex based solution methods and newer developments such as interior point methods are covered.

Product Description

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. Stochastic Linear Programming is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature.

Product Description
Along with the traditional material concerning linearprogramming (the simplex method, the theory of duality, the dualsimplex method), In-Depth Analysis of Linear Programmingcontains new results of research carried out by the authors.
For the first time, the criteria of stability (in the geometrical andalgebraic forms) of the general linear programming problem areformulated and proved. New regularization methods based on the idea ofextension of an admissible set are proposed for solving unstable(ill-posed) linear programming problems. In contrast to the well-knownregularization methods, in the methods proposed in this book theinitial unstable problem is replaced by a new stable auxiliaryproblem. This is also a linear programming problem, which can besolved by standard finite methods. In addition, the authors indicatethe conditions imposed on the parameters of the auxiliary problemwhich guarantee its stability, and this circumstance advantageouslydistinguishes the regularization methods proposed in this book fromthe existing methods. In these existing methods, the stability of theauxiliary problem is usually only presupposed but is not explicitlyinvestigated.
In this book, the traditional material contained in the first threechapters is expounded in much simpler terms than in the majority ofbooks on linear programming, which makes it accessible to beginners aswell as those more familiar with the area. ... Read more

Product Description
Includes one IBM/PC floppy disk. System Requirements: Monochrome monitors, IBM-compatible machines, minimum: 286 IBM, DOS 2.0 or higher. This book gives a complete, concise introduction to the theory and applications of linear programming. It emphasizes the practical applications of mathematics, and makes the subject more accessible to individuals with varying mathematical abilities. It is one of the first rigorous linear programming texts that does not require linear algebra as a prerequisite. In addition, this text contains a floppy disk containing the program SIMPLEX, designed to help students solve problems using the computer.

Key Features
* Less rigorous mathematically - will appeal to individuals with varying mathematical abilities
* Includes a floppy disk containing the program SIMPLEX and an appendix to help students solve problems using the computer
* Includes chapters on network analysis and dynamic programming - topics of great interest to business majors and industrial engineers
* Includes modem applications - selected computer programs for solving various max/min applications ... Read more

Customer Reviews (2)

Fun to read
Answers the question of what linear programming is used for in a very accessible example-based way. The book hasn't been written with heavyweights in the subject of mathematics and especially linear algebra inmind. Good college algebra course is enough to grasp most of the presentedconcepts. Basic Simplex, Lindo, game theory, transportation and networkproblems and many more are well explained. When you're through you'll gethooked on the subject and you'll have preparation for higher level stuff.

The best book I've read on how to set-up & solve LP problems
Alan Sultan has done something great!He has taken a complex subject, andmade it undestandable to the average person.

I've just started mygraduate program in Industrial Engineering, and need a good primer on LPproblem function.My background in Comp.Sci. did not prepare me for thestudy of I.E.I needed a good resource to explain the fundamentals suchthat they could be quicky and easily comprehended.

Mr. Sultan's textprovides a wonderful resource which helps me 'dechiper' my graduate leveltexts!Way to go, Alan!! ... Read more