Krause, E., RWTH Aachen, Germany
Jager, W., University of Heidelberg, Germany
(Eds.)

High Performance Computing in Science and Engineering '99

Transactions of the High Performance Computing Center, Stuttgart (HLRS) 1999

2000. XII, 500 pp.
3-540-66504-8

The book contains reports about the most significant projects from science and engineering of the
Federal High Performance Computing Center Stuttgart (HLRS). They were carefully selected in a
peer-review process and are showcases of an innovative combination of state-of-the-art modeling,
novel algorithms and the use of leading-edge parallel computer technology. The projects of HLRS
are using supercomputer systems operated jointly by university and industry and therefore a
special emphasis has been put on the industrial relevance of results and methods.

Keywords: High-Performance-Computing Scientific Computing, Numerical methods, Parallel computing

 


Langtangen, H.P., University of Oslo, Norway
Bruaset, A.M., Oslo, Norway
Quak, E., Oslo, Norway
(Eds.)

Advances in Software Tools for Scientific Computing

2000. IX, 360 pp. 138 figs., 32 tab.
3-540-66557-9

This book concerns programming techniques like object-oriented programming and generic
(template) programming. These modern techniques have proven to increase flexibility,
modularization, code reuse and improve maintenance of large numerical codes. The book contains
11 refereed and comprehensive chapters on major subjects in computational science and
engineering: quality measurement of numerical software, high-performance numerical computations
with C++ without sacrificing efficiency, a balanced discussion of Java in scientific computing,
object-oriented design of direct sparse solvers, geometric kernels in geographical information
systems, and tools for error estimation in finite element methods, tools for validating computational
results, and how to simplify the implementation of highly complex mathematical model for material
processing.

Keywords: scientific computing, object-oriented programming, numerical software C ++

Series: Lecture Notes in Computational Science and Engineering.VOL. 10

 


Chen, Z., University of Nebraska, Lincoln, NE, USA

Computational Intelligence for Decision Support

2000. Approx. 400 pp.
0-8493-1799-1

Decision support refers to applications involving comprehensive analysis and exploration of current
and historical data in organizations to support high-level decision making. Intelligent decision
support relies on many techniques provided by various disciplines, such as artificial intelligence and
database management systems. The conventional view on the role of AI and DBMS in decision
support is that decision support can be assisted by these techniques. Artificial Intelligence is the
science of building intelligent agents. Recently, an alternative term, "computational intelligence",
has gained in popularity. By emphasizing specific computational mechanisms underlying symbolic
reasoning process rather than focusing on controversial issues around symbolic reasoning itself,
computational intelligence provides a solid approach to effectively achieving many goals of artificial
intelligence.

Contents: Basics of decision support.- Basics of artificial intelligence and computational
intelligence.- Basics of information retrieval.- Basics of data structures.- An integrated framework of
computational intelligence for decision support.- Conceptual modeling.- Retrieval and reasoning for
computational creativity.- Conceptual queries and intensional answering.- Other topics.- An
overview on distributed DBMS.- Data mining.- Soft computing.- Other issues.- Meta data and meta
patterns.- Object-oriented paradigm.- Temporal aspects.- Spatial aspects.- Self organization.- Other
issues.- Integration and comparison of different techniques.- Integration and comparison of different
ways of thinking.- How to continue your work.

Series: International Series on Computational Intelligence.


Chen, M.-H., Worcester Polytechnic Institute, Worcester, MA, USA
Ibrahim, J.G., Harvard School of Public Health, Boston, MA, USA
Shao, Q.-M., University of Oregon, Eugene, OR, USA

Monte Carlo Methods in Bayesian Computation

2000. Approx. 400 pp. 20 figs.
0-387-98935-8

Dealing with methods for sampling from posterior distributions and how to compute posterior
quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such
topics as improving simulation accuracy, marginal posterior density estimation, estimation of
normalizing constants, constrained parameter problems, highest posterior density interval
calculations, computation of posterior modes, and posterior computations for proportional hazards
models and Dirichlet process models. The authors also discuss model comparisons, including both
nested and non-nested models, marginal likelihood methods, ratios of normalizing constants,
Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian
Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent
residual approaches. The book presents an equal mixture of theory and applications involving real
data, and is intended as a graduate textbook or a reference book for a one-semester course at the
advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical
researchers as well as practitioners.

Contents: Introduction.- Markov Chain Monte Carlo Sampling.- Basic Monte Carlo Methods for
Estimating Posterior Quantities.- Estimating Marginal Posterior Densities.- Estimating Ratios of
Normalizing Constants.- Monte Carlo Methods for Constrained Parameter Problems.- Computing
Bayesian Credible and HPD Intervals.- Bayesian Approaches for Comparing Non-Nested Models.-
Bayesian Variable Section.- Other Topics.

Series: Springer Series in Statistics.


Meduna, A., Technical University of Brno, Czech Republic

Automata and Languages ;Theory and Applications

2000. XVI, 920 pp.
1-85233-074-0

A step-by-step development of the theory of automata, languages and computation. Intended for
use as the basis of an introductory course at both junior and senior levels, the text is organized so
as to allow the design of various courses based on selected material. It features basic models of
computation, formal languages and their properties; computability, decidability and complexity; a
discussion of modern trends in the theory of automata and formal languages; design of
programming languages, including the development of a new programming language; and compiler
design, including the construction of a complete compiler. Alexander Meduna uses clear
definitions, easy-to-follow proofs and helpful examples to make formerly obscure concepts easy to
understand. He also includes challenging exercises and programming projects to enhance the
reader's comprehension, and many 'real world' illustrations and applications in practical computer
science.

Contents: INTRODUCTION: Languages. Formalization of Languages. Expressions and Grammars.
Translations. Exercises, Programming Projects. Automata. Conceptualization of Automata.
Transducers. Computability. Exercises. Programming Projects. Bibliographic Notes.- REGULAR
LANGUAGES: Models for Regular Languages. Regular Expressions. Finite Automata. Finite
Automata and Regular Expressions. Exercises. Programming Projects. Properties of Regular
Languages. Pumping Lemma. Closure Properties. Decidable Problems. Exercises.-
CONTEXT-FREE LANGUAGES: Models for Context-Free Languages. Context-Free Grammars.
Pushdown Automata. Pushdown Autmata and Context-Free Grammars. Exercises. Programming
Projects. Properties of Context-Free Languages. Pumping Lemma. Closure Properties. Decidable
Problems. Exercises. Special Types of Context-Free Languages and Their Models. Deterministic
Context-Free Languages. Linear and Regular Grammars. Exercises.- BEYOND CONTEXT-FREE
LANGUAGES: Generalized Models. Turing Machines. Two-Pushdown Automata. Unrestricted
Grammars. A Hierarchy of Language Families. Exercises. Programming Projects. Bibliographic
Notes.- TRANSLATIONS: Finite and Pushdown Transducers. Finite Transducers. Translation
Grammars and Pushdown Transducers. Compilers. Exercises. Programming Projects. Turing
Transducers. Basic Definitions. Computability. Decidability. Exercises. Programming Projects.-
Bibliographic Notes.- Appendix - Mathematical Background.- Bibliography.- Indices.- Index to
Special Symbols.- Index to Decision Problems.- Index to Algorithms.- Subject Index.


Rynne, B.P., Heriot-Watt University, Edinburgh, UK
Youngson, M.A., Heriot-Watt University, Edinburgh, UK

Linear Functional Analysis

2000. Approx. 240 pp.
1-85233-257-3

Providing an introduction to the ideas and methods of linear functional analysis, this book shows
how familiar and useful concepts from finite-dimensional linear algebra can be extended or
generalized to infinite-dimensional spaces. In the initial chapters, the theory of infinite-dimensional
normed spaces (in particular Hilbert spaces) is developed, while in later chapters the emphasis
shifts to studying operators between such spaces. Functional analysis has applications to a vast
range of areas of mathematics; the final chapter discusses the two particularly important areas of
integral and differential equations. The reader is assumed to have a standard undergraduate
knowledge of linear algebra, real analysis (including the theory of metric spaces), and Lebesgue
integration. An introductory chapter summarizes the requisite material. Many exercises are
included with solutions provided for each.

Contents: Preliminaries.- Normed Spaces.- Inner Product Spaces, Hilbert Spaces.- Linear
Operators.- Linear Operators on Hilbert Spaces.- Compact Operators.- Integral and Differential
Equations.- Solutions to Exercises.- Further Reading.- References.- Notation Index.- Index.

Series: Springer Undergraduate Mathematics Series.

 


Paul, W., University of Mainz, Germany
Baschnagel, J., Institut Charles Sadron, Strasbourg, France

Stochastic Processes ; From Physics to Finance

2000. XIV, 234 pp. 36 figs.
3-540-66560-9

This book presents an introduction to stochastic processes with applications from physics and
finance. It introduces the basic notions of probability theory and the mathematics of stochastic
processes. The applications that we discuss are chosen to show the interdisciplinary character of
the concepts and methods, and are taken mainly from physics and finance. Due to its
interdisciplinary character and choice of topics, the book can show students and researchers in
physics how models and techniques used in their field can be translated into and applied in the
field of finance and risk-management. On the other hand, a practitioner from the field of finance will
find models and approaches recently developed in the emerging field of econophysics for
understanding the stochastic price behavior of financial assets.

Keywords: Stochastic Processes, Random Walks, Levy Flights, Finance, Computer Simulation

Contents: 1. A First Glimpse of Stochastic Processes; 2. A Brief Survey of the Mathematics of
Probability Theory; 3. Diffusion Processes; 4. Beyond the Central Limit Theorem: Levy
Distributions; 5. Modeling the Financial Market; Appendices


Good, P., Huntington Beach, CA, USA

Permutation Tests , 2nd ed.

A Practical Guide to Resampling Methods for Testing Hypotheses

2nd ed. 2000. Approx. 345 pp. 16 figs.
0-387-98898-X

A step-by-step manual on the application of permutation tests in biology, business, medicine,
science, and engineering. Its intuitive and informal style make it ideal for students and researchers,
whether experienced or coming to these resampling methods for the first time. The real-world
problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact
covariates, and outliers are all dealt with at length. This new edition has more than 100 additional
pages, and includes streamlined statistics for the k-sample comparison and analysis of variance
plus expanded sections on computational techniques, multiple comparisons, multiple regression,
comparing variances, and testing interactions in balanced designs. The comprehensive author and
subject indexes, plus an expert-system guide to methods, provide for further ease of use, while the
exercises at the end of every chapter have been supplemented with drills and a number of
graduate-level thesis problems.

Contents: A Wide Range of Applications.- A Simple Test.- Testing Hypotheses.- Experimental
Designs.- Multivariate Analysis.- Categorical Data.- Dependence.- Clustering in Time and Space.-
Coping with Disaster.- Which Statistic? Solving the Insolvable.- Which Test Shoud You Use?.-
Publishing Your Results.- Increasing Computational Efficiency.- Theory of Permutation Tests.

Series: Springer Series in Statistics.

 


Aldous, J., The Open University, Milton Keynes, UK
Wilson, R., The Open University, Milton Keynes, UK

Graphs and Applications

An Introductory Approach

2000. Approx. 455 pp. 650 figs.
1-85233-259-X

Discrete Mathematics is one of the fastest growing areas in mathematics today with an
ever-increasing number of courses in schools and universities. Graphs and Applications is based
on a highly successful Open University course and the authors have paid particular attention to the
presentation, clarity and arrangement of the material, making it ideally suited for independent study
and classroom use. An important part of learning graph theory is problem solving; for this reason
large numbers of examples, problems (with full solutions) and exercises (without solutions) are
included.
Accompanying the book is a CD ROM comprising a Graphs Database, containing all the simple
unlabelled graphs with up to seven vertices, and a Graphs Editor that enables students to construct
and manipulate graphs. Both the Database and Editor are simple to use and allow students to
investigate graphs with ease. Computing Notes and suggested activities are provided.

Contents: 1. Introduction.- 2. Graphs.- 3. Eulerian and Hamiltonian Graphs.- 4. Digraphs.- 5. Matrix
Representations.- 6. Tree Structures.- 7. Counting Trees.- 8. Greedy Algorithms.- 9. Path
Algorithms.- 10. Connectivity.- 11. Planarity.- 12. Vertex Colourings and Decompositions.- 13.
Edge Colourings and Decompositions.- 14. Conclusion.- Suggestions for Further Reading.-
Appendix: Methods of Proof.- Solutions to the Problems.- Computer Notes.- Index.

 


Bouwmeester, D., University of Oxford, UK
Ekert, A.K., Oxford, UK
Zeilinger, A., University of Vienna, Austria
(Eds.)

The Physics of Quantum Information

Quantum Cryptography, Quantum Teleportation, Quantum Computation

2000. Approx. 275 pp. 100 figs.
3-540-66778-4

Leading experts from "The Physics of Quantum Information" network, an initiative of the European
Commission, bring together the most recent results of the emerging area of quantum technology.
Written in a consistent style as a research monograph, the book introduces into quantum
cryptography, quantum teleportation, and quantum computation, considering both theory and
newest experiments. Thus scientists working in the field and advanced students will find a rich
source of information on this exciting new area.

Keywords: Quantum Cryptography, Quantum Communication, Quantum Computation, Quantum
Cryptology, Quantum Optics

Contents: Basic Concepts in Quantum Information Physics.- Quantum Cryptography.- Quantum
Dense Coding and Quantum Teleportation.- Concepts of Quantum Computation.- Experiments
Towards Quantum Computation.- Quantum Networks and Many-Particle Entanglement.-
Decoherence and Quantum Error Correction.- Entanglement Purification.- References.- Index