Book Description
- Unique in its survey of the range of topics.
- Contains a strong, interdisciplinary format that will appeal to both students and researchers.
- Features exercises and web links to software and data sets.
Download Description
- Unique in its survey of the range of topics.
- Contains a strong, interdisciplinary format that will appeal to both students and researchers.
- Features exercises and web links to software and data sets.
Customer Reviews:
Great book!!!.......2004-12-07
A must have for anyone interested in otimization! Extremely well written and objective.
Recommended to scholars and graduate students.......2003-09-23
Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas.
Book Description
Increasingly, educators are turning to Mathematica for instructing students in science and mathematics. The growing popularity of this exciting software package creates a need for undergraduate students to be familiar with its many functions and uses. The Student's Introduction to Mathematica® neatly follows a standard mathematics curriculum, allowing students to practice what they learn. The text lucidly presents those aspects of the software that are the most useful to students. Mathematica commands are introduced as a means of solving problems and illuminating the underlying mathematical principles. Following a brief introduction to the program, Bruce and Eve Torrence discuss functions and graphs, algebra, calculus, and multivariate calculus, and finish with a chapter on linear algebra. No prerequisites other than high school level mathematics are assumed. This work can be used in a variety of courses, from precalculus, through single and multi-variable calculus, to linear algebra. In addition to its course use, this book is an excellent tutorial for those wishing to learn Mathematica and brush up on their mathematics at the same time. The volume is compatible with Mathematica version 3 and higher.
Customer Reviews:
A Great First Step.......2006-12-02
This book is designed to jump start the reader's experience with Mathematica. Since people learn best by example, reviewing "basic" math examples (Algebra, Calculus, and Linear Algebra) provides a "painless" introduction to the new environment. This book is not intended to be complete in any sense. But it WILL make you feel comfortable with the daunting new interface of Mathematica.
This is the only Mathematica book that I have finished. It was a great introduction, and I still use as a reference.
If you are thinking about buying Mathematica--DON'T get it without some introductory book. This one as a good start.
Product Description
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.
Customer Reviews:
Par Excellence!.......2006-11-14
This book is THE best LP book I have come across. The topics are very clear and presented in the best possible manner. Introduces you to several basic and advanced LP topics, theorems and algorithms. The exercises at the end of each chapter test the students' understanding in an appropriate manner. A good number of examples are given to explain the theory in a better way. I would definitely recommend this book to a student interested in learning about optimization procedures and/or algorithm development.
Surely helps if you have taken a linear algebra course before. Some students who haven't had a linear algebra course find the math nomenclature formidable in the beginning.
Quite good.......2006-08-01
This book is impressive for theory, every thing you ever wanted to know or how to avoid some other is here. I teach to industrial engineering students, so i have to use other books for the application, but for the theory, everything is covered here, even more, in the book are several simple rules to avoid tipical problems of the simplex method or transportation problems, or integer forms. You can't call yourself a pro if you haven't read this book.
Nice intuition and good coverage.......2006-01-01
The best part of this book is the first half, where the foundations of linear programming are presented in a clear yet relatively rigorous fashion, accompanied by numerous intuitive geometrical explanations of the abstract general concepts. This approach, supplementing mathematics with graphical insights, works extremely well for this topic.
The quality goes down somewhat, perhaps neccessarily, in the latter half of the book as topics are presented less carefully, and in a somewhat rushed manner in order to cover all of the material the authors decided to include. Given that the fundamentals are covered so well, perhaps this is a fair trade.
The only real negative I can think of is that it's a small crime for professors to create their own publishing companies (Athena only publishes works by a small group of MIT professors) and then still charge outrageous amounts for the books. This would be completely unacceptable were it not for the fact that, unlike most self-published work, this book's production quality is on par with that of the large publishers.
Too Verbose.......2005-12-17
Most part can be expressed in a more terse way and with math language. However, the book revolve around using very lengthy sentence to explain, which is not so helpful and clear as expressed with math. It can be condensed to half thickness.
A+++.......2005-09-24
Exactly as described, fast delivery. I will always try to choose amazon from now on.
Book Description
Generalized linear models provide a unified theoretical and conceptual framework for many of the most commonly used statistical methods. In the ten years since publication of the first edition of this bestselling text, great strides have been made in the development of new methods and in software for generalized linear models and other closely related models. Thoroughly revised and updated, An Introduction to Generalized Linear Models, Second Edition continues to initiate intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy and examples from an even wider range of application. Statistics can appear to the uninitiated as a collection of unrelated tools. An Introduction to Generalized Linear Models, Second Edition illustrates how these apparently disparate methods are examples or special cases of a conceptually simple structure based on the exponential family of distribution, maximum likelihood estimation, and the principles of statistical modelling.
Customer Reviews:
Clear and Consice but too Compact.......2004-11-20
While what the book does explain about the statistical theory mentioned, it is too compact for what it tries to explain. There are also no answers to the excercises, which would be quite helpful given some of the questions asked. It's great for applications and is a good handbook, but for a thorough explanation of everything involved, I recomend getting a bigger textbook! For my 4th year Generalized Linear Models stats class, this book is helpful, but at times too compact to be more useful.
the most clearly written book on the topic.......2002-08-16
My copy of the second edition just arrived yesterday and it is even better than the first edition (which was fantastic). The logical organization and clarity of writing make this book a 'must have' for any statistician's library. I'd give it 6 stars if I could. Readers should also check out McCulloch and Searle's 'Generalized, Linear and Mixed Models'.
Excellent concept - Execution could be better.......2002-08-04
I wish somebody would write a concise tutorial of the matematics required for an "intermediate" book such as Dobson's. Undoubtedly for someone whose acquaintence with modern statitical methods is more current this book is a gem. For someone like myself who wants a refresher and whose math is a bit rusty it leaves something to be desired. Some of the theoretical derivations in chapters 3 and 4 (keys to the understanding of the rest of the book) would be improved by a bit more detail and a thoroughly worked example. A major shortcoming is the lack of answers to the excercises; I don't see how the book was published without them. If the book was 100 pages longer with the addition of the aforementioned material, I would have given it a five star rating.
recommended for applications and clarity.......2002-06-18
...
Bill recommended Dobson's text because of her clear writing style and many useful examples. Dobson also places the theory in the context of the general exponential family of distributions. As I knew that the second edition was about to come out I waited for it.
The wait seems to have been very worthwhile. The second edition is a real bargin.... She has updated it with the many advances that have occurred over the past 12 years since the first edition was printed. This edition now includes some discussion of generalized additive models, broader coverage of applications as survival analysis, GEE, multi-level models and nominal and ordinal logistic regression have been added. It now offers the reader more applications in a wider variety of disciplines and includes modern approaches to diagnostic checking of the models.
As with the first edition, exploratory techniques are emphasized particularly graphical methods. The goal is to unify the apparently disparate statistical techniques that students are exposed to, into one general modeling framework.
It includes a nice up-to-date bibliography and recent advanced results on longitudinal models. The level is intermediate statistics with introductory statistics and linear models taken to be prerequisites. Students are also required to have some familiarity with calculus and linear algebra.
-.......2000-04-04
This book provides a surprisingly brief and gentle, yet thorough, introduction to the subject of modeling dependent variables that are not continuous (see note below). The reader, who should be familiar with calculus-based probability, may initially find it frustrating that the actual practice of modeling nominal data is not discussed until the last two chapters (of 9). However, the cause for delaying the discussion of these models is to introduce the terminology and methodology of generalized linear models through more familiar linear regression models.
Thus, while this book is not ideal for someone who wants to jump right into the thick of building logistic, loglinear, or other models for nominal data, it is quite suitable for those wishing a thorough introduction to the practice of generalized linear modeling. For greater detail, a thicker book like McCullagh & Nelder's _Generalized Linear Models_ would be suitable.
Note: While the term "Generalized Linear Models" includes linear regression models (i.e., models for continuous dependent variables), reading this book is not the easiest way to be introduced to regression. A better starting point would be Draper & Smith's _Applied Regression Analysis_ or Weisberg's _Applied Linear Regression_.
Average customer rating:
- It reads like source code
- Rigor-Envy
- Not For Undergraduates
- took the class, liked the book
- All industrial engineering student should buy this book.
|
An Introduction to Optimization, 2nd Edition
Edwin K. P. Chong , and
Stanislaw H. Żak
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Applied Optimization with MATLAB Programming
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ASIN: 0471391263 |
Book Description
A modern, up-to-date introduction to optimization theory and methods
This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.
Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:
* A review of the required mathematical background material
* A mathematical discussion at a level accessible to MBA and business students
* A treatment of both linear and nonlinear programming
* An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
* A chapter on the use of descent algorithms for the training of feedforward neural networks
* Exercise problems after every chapter, many new to this edition
* MATLAB(r) exercises and examples
* Accompanying Instructor's Solutions Manual available on request
An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Download Description
A modern, up-to-date introduction to optimization theory and methods
This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.
Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:
* A review of the required mathematical background material
* A mathematical discussion at a level accessible to MBA and business students
* A treatment of both linear and nonlinear programming
* An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
* A chapter on the use of descent algorithms for the training of feedforward neural networks
* Exercise problems after every chapter, many new to this edition
* MATLAB(r) exercises and examples
* Accompanying Instructor's Solutions Manual available on request
An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.
Customer Reviews:
It reads like source code.......2007-04-18
I'm an undergraduate math major who is using this book in a linear programming course. The general consesus in my class is that this is a very difficult book to comprehend. Everything seems like it's been abstracted to the n-th degree. Variables are frequently used without reference to definitions, which in many cases appear in earlier sections. It's a pain to try to look up something then have to hunt around for the meaning of all the components used in the definition. That's not to say this book isn't informative, it just takes a lot of work to glean useful information from it. As a student, I prefer books that are easy to reference. I simply don't have time to read the whole chapter about the simplex method when I just want to know how to compute cost coefficients.
Rigor-Envy.......2007-03-14
I can only speak on the linear programming section in this book. This is an awful text for undergraduates. This is a math text written by engineers who have a huge case of mathematical rigor-envy. They sacrifice all context, specificity, and practicality in lieu of a ridiculus level of mathematical generality. I am experienced in upper division proofing. I found myself reading and understanding every line of the proofs( of which there are many!) and still having no idea what had just been demonstrated. If you already have a PhD in pure mathematics, then this might be the book for you. If you are an undergraduate, stay away! If you need this book for a linear programming course, do youself a favor and also buy Linear Programming be Vasek Chvatal. The Chvatal text is the premier text on LP. It's only disadvantage is that it does not cover interior point methods, but this material can be easily supplemented from other sources. If yor are a prof. and are considering using this book for a undergraduate course, don't. Do your students some good and use a better text.
Not For Undergraduates.......2004-04-23
This book should not be used to teach an Introduction to Optimization at the undergraduate level. It is being done so at my school, and it is driving the undergraduate students crazy because they do not understand the book, the notation also is causing problems. If you are new to the subject area, and do not have an advanced math background(more than college) try looking elsewhere.
took the class, liked the book.......1999-04-30
Drs. Chong and Zak are Professors of Electrical Engineering at Purdue, and Dr. Chong was the instructor for the ECE grad level optimization class when I took it spring '97. The book alone is good, detailed and rigorous enough for a graduate course without sacrificing readability or in-chapter examples. However, without the MATLAB examples that were developed by the authors to accompany lectures and illustrate each optimization method covered, the material might be a little abstract or dry for self-teaching. An excellent introduction or reference nonetheless, those without a solid base in linear algebra should keep a reference text handy while reading.
All industrial engineering student should buy this book........1997-12-22
An Introduction to Optimization
Average customer rating:
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Introduction to Optimal Control Theory (Undergraduate Texts in Mathematics)
Jack Macki , and
Aaron Strauss
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Calculus of Variations: Mechanics, Control, and Other Applications
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ASIN: 038790624X |
Book Description
This is an introduction to optimal control theory for systems governed by vector ordinary differential equations, up to and including a proof of the Pontryagin Maximum Principle. Though the subject is accessible to any student with a sound undergraduate mathematics background. Theory and applications are integrated with examples, particularly one special example (the rocket car) which relates all the abstract ideas to an understandable setting. The authors avoid excessive generalization, focusing rather on motivation and clear, fluid explanation.
Book Description
Dynamic Modeling for Business Management
applies dynamic modeling to business management, using accessible modeling techniques that are demonstrated starting with fundamental processes and advancing to more complex business models. Discussions of modeling emphasize its practical use for decision making and implementing change for measurable results. Readers will learn about both manufacturing and service-oriented business processes using hands-on lessons. They will then be able to manipulate additional models to try out their knowledge and address issues specific to their own businesses and interests. All of the models used in the book along with demo versions of ithink® and Berkeley Madonna™ software are included with the book on a CD-ROM. Some of the topics covered include workflow management, supply-chain management, and business strategy.
Customer Reviews:
Good book but poor choice of modeling software........2007-04-04
Interesting approach to modeling but it's all dependent on ithink, which is an absolutely old software program with a user interface from the Win 3.1 era. You'll be banging your head against the wall at the archaic nature of the program. Would've been a great program ten+ years ago but today I can't imagine anyone seriously wanting to learn this software. And in all honesty, the financial modeling section is hokey, as the manipulations are much easier done with excel.
Great Book on Business Systems and Simulation.......2005-07-20
Dynamic Modeling for Business Management is an excellent reference book for business and industrial engineer students and practitioners. The book provides explanations and examples for adding dynamics to some of the basic business systems and processes. The mathematical models introduced in operations research and management science courses have very restrictive assumptions. Simulation allows exploring more realistic models and enables the reader the appreciation the value of adding more realism of systems by having the capability of more realistic properties and feedback mechanisms. An example is waiting lines. In most real situations, the service rate is affected by the current length of the waiting line. Queuing models ignore this. Using simulation, you can model the results of these interdependencies.
The authors include Little's Law and serial workflow processes along with material flows, supply chains, repair systems, batching and tradeoffs among quality, speed and costs. This is the dynamic example of the old adage: "You can have it good, fast, cheap; pick two."
This book serves as an introduction to the simulation language ithink by iseesystems, formally High Performance Systems. The advantage of using this continuous language is in its simplicity, which also makes it both easy to learn and very adaptable. Good for readers that are not familiar with the language. This book is part of the Springer series on Modeling Dynamic Systems.
Excellent text for applies dynamic modeling.......2005-07-18
This is an excellent textbook for an upper-level undergraduate or graduate course in business simulation modeling. It might also be useful in an engineering course on simulation modeling, especially in more applied areas like industrial engineering.
I find the book more accessible, if not as complete, than Sterman's text. By adopting a "hand's on" approach and using the i-Think modeling software, I believe students will also find the concepts more accessible. For users of i-Think, this text has some excellent examples of how the software can be used to model specific manufacturing and supply chain business problems. Even if you aren't an i-Think user (it isn't my first choice) you will still find a lot of practical modeling advice. The math is solid, and there is an interesting twist on Little's Law. For those new to stochastic modeling, the section on modeling random variation is priceless. It takes the reader through the most useful modeling distributions and is a wonderful blend of mathematics and applied modeling.
My only negative comments relate to the meager section on Economic Value Added (which should have been more fully developed and moved into the main body of the text--in my opinion), and the selection of software (no offense to the i-Think users, but I think there are better tools for general business simulation modeling. Though maybe not for dynamic modeling--so I temper my criticism.)
In short, this is an excellent entry point for the reader who wants a solid introduction to dynamic systems and their specific application to business.
Good Book and Can Easily Be Put into Practice..........2004-05-25
I read this after Sterman's 'Business Dynamics'. While 'Business Dynamics' is a graduate level text book, this book is more aligned to the requirements of practitioners like consultants and managers. MDS have a series of books on use of dynamic modeling, each focusing on a specific area like biological systems, economics, health sciences, environmental systems etc.
Customer Reviews:
Good Examples BUT...a little too theoretical.......2000-08-02
I've used this book for a graduate course in Dynamic Programming. Having used many of Mr. Ross's books (undergraduate and graduate), I found this one lacks the detail and lucidity (particularly the end of chapter problems...I believe in "learning by doing"... i.e. solve lots of problems!) that I have come to know of his books (e.g. A First Course in Probability and Introduction to Probability Models). The bright spot of the book is its examples, which are interesting and fairly detailed.
Average customer rating:
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Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms (Applied Optimization)
Jan A. Snyman
Manufacturer: Springer
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This book presents basic optimization principles and gradient-based algorithms to a general audience in a brief and easy-to-read form, without neglecting rigor. The work should enable professionals to apply optimization theory and algorithms to their own particular practical fields of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties – such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima – that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods.
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Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Stefan Voß , and
David L. Woodruff
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Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies
ASIN: 3540298789 |
Book Description
The book begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation fairly sophisticated models for supply chain management are developed. Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages. The new edition is updated and provides extensions.
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