extend the range of problems they can solve (and thereby the range of
applications they can address) using the methods developed here.
Modern Experimental Stress Analysis:
— Presents a comprehensive and modern reformulation of the approach to
processing experimental data
— Offers a large collection of problems ranging from static to dynamic, linear
to non-linear
— Covers stress analysis with the finite element method
— Includes a wealth of documented experimental examples
— Provides new ideas for researchers in computational mechanics
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William Gropp, et al, "The Sourcebook of Parallel Computing"
Morgan Kaufmann | ISBN 1558608710 | 2002 Year | PDF | 5,88 Mb | 842 Pages
“ Sourcebook of Parallel Computing is an indispensable reference for parallel-
computing consultants, scientists, and researchers, and a valuable addition to
any computer science library.
-Distributed Systems Online
"The Sourcebook for Parallel Computing gives a thorough introduction to parallel
applications, software technologies, enabling technologies, and algorithms. This
is a great book that I highly recommend to anyone interested in a comprehensive
and thoughtful treatment of the most important issues in parallel computing. "
-Horst Simon, Director, Director, NERSC, Berkeley
"The Sourcebook builds on the important work done at the Center for Research on
Parallel Computation and within the academic community for over a decade. It is
a definitive text on Parallel Computing and should be a key reference for
students, researchers and practitioners in the field."
-Francine Berman, Director, San Diego Supercomputer Center and the National
Partnership for Advanced Computational Infrastructure
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Lance Chambers (Editor), "The Practical Handbook of Genetic Algorithms. Vol. I-
III"
Chapman & Hall / CRC Press | ISBN 1584882409/0849325293/0849325390 | 1995-2000
Years | DjVu | 14,92 Mb | 544/435/592 Pages
“ Rapid developments in the field of genetic algorithms along with the
popularity of the first edition precipitated this completely revised, thoroughly
updated second edition of The Practical Handbook of Genetic Algorithms. Like its
predecessor, this edition helps practitioners stay up to date on recent
developments in the field and provides material they can use productively in
their own endeavors. For this edition, the editor again recruited authors at the
top of their field and from a cross section of academia and industry, theory and
practice. Their contributions detail their own research, new applications,
experiment results, and recent advances. Among the applications explored are
scheduling problems, optimization, multidimensional scaling, constraint
handling, and feature selection and classification. The science and art of GA
programming and application has come a long way in the five years since
publication of the bestselling first edition. But there still is a long way to
go before its bounds are reached-we are still just scratching the surface of GA
applications and refinements. By introducing intriguing new applications,
offering extensive lists of code, and reporting advances both subtle and
dramatic, The Practical Handbook of Genetic Algorithms is designed to help
readers contribute to scratching that surface a bit deeper.
The mathematics employed by genetic algorithms (GAs)are among the most exciting
discoveries of the last few decades. But what exactly is a genetic algorithm? A
genetic algorithm is a problem-solving method that uses genetics as its model of
problem solving. It applies the rules of reproduction, gene crossover, and
mutation to pseudo-organisms so those "organisms" can pass beneficial and
survival-enhancing traits to new generations. GAs are useful in the selection of
parameters to optimize a system's performance. A second potential use lies in
testing and fitting quantitative models. Unlike any other book available, this
interesting new text/reference takes you from the construction of a simple GA to
advanced implementations. As you come to understand GAs and their processes, you
will begin to understand the power of the genetic-based problem-solving
paradigms that lie behind them.The Practical Handbook of Genetic Algorithms
presents for the first time new areas of research and implementation. Problems
that for many have been considered intractable are shown to be solvable using
the techniques described in this work. Specific solution descriptions to real-
world problems are provided, or use these as examples to develop solutions to
unique problems.Volume II picks up where the first book leaves off and presents
the topic from more of an applications point of view. The focus of the book is
to show the reader how to develop their own genetic algorithm coding schemes and
how and when to employ the GA to solve problems.
Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems
contains computer-code examples for the development of genetic algorithm systems
- compiling them from an array of practitioners in the field.Each contribution
of this singular resource includes:ounique code
segmentsodocumentationodescription of the operations performedorationale for the
chosen approachoproblems the code overcomes or addressesPractical Handbook of
Genetic Algorithms, Volume 3: Complex Coding Systems complements the first two
volumes in the series by offering examples of computer code. The first two
volumes dealt with new research and an overview of the types of applications
that could be taken with GAs. This volume differs from its predecessors by
specifically concentrating on specific functions in genetic algorithms, serving
as the only compilation of useful and usable computer code in the field.
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Philip Schneider, David H. Eberly, "Geometric Tools for Computer Graphics"
Morgan Kaufmann | ISBN 1558605940 | 2002 Year | PDF | 4,85 Mb | 1056 Pages
“ Reinventing the wheel is a terrible waste of time, yet legions of computer
programmers do exactly that every day. Geometric Tools for Computer Graphics
gives the working graphics programmer a vast collection of programming examples,
complex code snippets explained and ready to use. Each chapter is filled with
more than just code examples — the explanations needed to understand why these
examples work the way they do are given by authors with experience both in
writing and in the field.
There is nothing here for the casual graphics programmer and everything for the
serious 2-D and 3-D programmer. Thirteen chapters, three appendices, and a
three-column index that spans over 30 pages cover everything about computer-
graphic geometry, from the basics of using matrices and linear systems to
intersecting 3-D objects.
The appendices alone are worth the price: "Numerical Methods," "Trigonometry,"
and "Basic Formulas for Geometric Primitives" are treasures filled with hard-
core examples of the kind that can be put to use right out of the box. Less
experienced programmers will find these to be invaluable references, but then
there's the rest of the book--nearly 1,000 pages loaded with examples and
theory, page after page of information written in a clear, concise voice.
Any hard-core graphics programmer will appreciate the value of the examples
presented here, as well as the discussion of theory. After all, there's no need
to waste time experimenting with code once the theory is known. Geometric Tools
represents the best of both worlds: discussion of theory and code examples built
on and culled from years of experience.
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Hagit Attiya, Jennifer Welch, "Distributed Computing: Fundamentals, Simulations,
and Advanced Topics" (2nd edition)
Wiley-Interscience | ISBN 0471453242 | 2004 Year | DjVu | 4,23 Mb | 432 Pages
“ "...the authors take readers through these notoriously difficult subjects and
ably demystify puzzling buzzwords…"
(IEEE Distributed Systems Online, March 2005)
"The authors present the fundamental issues underlying the design of distributed
systems…as well as fundamental algorithmic concepts and lower-bound techniques."
(IEEE Computer Magazine, October 2004)
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Hosam M. Mahmoud, "Sorting: A Distribution Theory"
Wiley-Interscience | ISBN 0471327107 | 2000 Year | DjVu | 3,31 Mb | 394 Pages
“ A cutting-edge look at the emerging distributional theory of sorting
Research on distributions associated with sorting algorithms has grown
dramatically over the last few decades, spawning many exact and limiting
distributions of complexity measures for many sorting algorithms. Yet much of
this information has been scattered in disparate and highly specialized sources
throughout the literature. In Sorting: A Distribution Theory, leading authority
Hosam Mahmoud compiles, consolidates, and clarifies the large volume of
available research, providing a much-needed, comprehensive treatment of the
entire emerging distributional theory of sorting.
Mahmoud carefully constructs a logical framework for the analysis of all
standard sorting algorithms, focusing on the development of the probability
distributions associated with the algorithms, as well as other issues in
probability theory such as measures of concentration and rates of convergence.
With an emphasis on narrative rather than technical explanations, this
exceptionally well-written book makes new results easily accessible to a broad
spectrum of readers, including computer professionals, scientists,
mathematicians, and engineers.
Sorting: A Distribution Theory:
— Contains introductory material on complete and partial sorting
— Explains insertion sort, quick sort, and merge sort, among other methods
— Offers verbal descriptions of the mechanics of the algorithms as well as the
necessary code
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