Detlef Lehmann TU Berlin FB Mathematik, Berlin, Germany

Mathematical Methods of Many-Body Quantum Field Theory

Series: Research Notes in Mathematics Series Volume: 436

ISBN: 1-58488-490-8
Publication Date: 8/30/2004
Number of Pages: 264

Offers a comprehensive, mathematically rigorous treatment of many-body physics
Presents many new results and clarifies difficult concepts
Provides a full introduction to the Feldman, Knoerrer & Trubowitz Fermio-liquid construction
Follows a methodical, step-by-step method of calculation and definition

Mathematical Methods of Many-Body Quantum Field Theory offers a comprehensive, mathematically rigorous treatment of many-body physics. It develops the mathematical tools for describing quantum many-body systems and applies them to the many-electron system. These tools include the formalism of second quantization, field theoretical perturbation theory, functional integral methods, bosonic and fermionic, and estimation and summation techniques for Feynman diagrams. Among the physical effects discussed in this context are BCS superconductivity, s-wave and higher l-wave, and the fractional quantum Hall effect. While the presentation is mathematically rigorous, the author does not focus solely on precise definitions and proofs, but also shows how to actually perform the computations.

Presenting many recent advances and clarifying difficult concepts, this book provides the background, results, and detail needed to further explore the issue of when the standard approximation schemes in this field actually work and when they break down. At the same time, its clear explanations and methodical, step-by-step calculations shed welcome light on the established physics literature.

Oliver Schabenberger SAS Institute Inc., Cary, North Carolina, USA
Carol A Gotway National Center for Environmental Health, Georgia, USA

Statistical Methods for Spatial Data Analysis

Series: Texts in Statistical Science Series Volume: 64

ISBN: 1-58488-322-7
Publication Date: 12/22/2004
Number of Pages: 504

Covers the three main types of spatial data: geostatistical data, lattice data, and point patterns in theory and application
Adopts a modeling approach that ties the material into well known areas like linear models and Monte Carlo testing
Requires no previous exposure to measure theory and includes the necessary background material from stochastic process theory

Spatial statistics -- one of the fastest growing areas of statistics -- has applications across a wide range of disciplines. Statistical Methods for Spatial Data Analysis takes a model-based approach and covers the theory and applications of all three major types of spatial data: geostatistical, lattice, and point pattern. The book is rich in applications and incorporates discussions on important recent developments in the field, such as spatial models for nonstationary data and generalized linear model extensions to spatial data. The authors also incorporate the use of SAS for problem solving and make the relevant code and various data sets available for download from the publisher's Web site.

Table of Contents

Introduction. Mapped Point Patterns. Semivariogram Analysis and Estimation. Geostatistics I. Spatial Prediction and Ordinary Kriging. Spatial Regression Models. Geostatistics II. Kriging Methods. Models for Lattic Data. Miscellaneous Topics. Appendices.

Roman Wienands University of Cologne, Kozu, Germany
Wolfgang Joppich University of Applied Science, Sankt Augustin, Germany

Practical Fourier Analysis for Multigrid Methods

Series: Numerical Insights Volume: 4
ISBN: 1-58488-49-24
Publication Date: 10/27/2004
Number of Pages: 240

Provides a theoretical framework necessary for the successful use of multigrid methods for (systems of) partial differential equations
Allows for local Fourier analysis via a simple mouse click, courtesy of accompanying software (LFA) and GUI (xlfa)
Includes case studies for two- and three-dimensional problems, including Poisson, convection diffusion, and biharmonic equation, the Oseen and Stokes equations, a linear shell problem and elasticity systems
Presents the recently-developed three-grid analysis, allowing investigation of real multigrid effects

Before applying multigrid methods to a project, mathematicians, scientists, and engineers need to answer questions related to the quality of convergence, whether a development will pay out, whether multigrid will work for a particular application, and what the numerical properties are. Practical Fourier Analysis for Multigrid Methods uses a detailed and systematic description of local Fourier k-grid (k=1,2,3) analysis for general systems of partial differential equations to provide a framework that answers these questions.

This volume contains software that confirms written statements about convergence and efficiency of algorithms and is easily adapted to new applications. Providing theoretical background and the linkage between theory and practice, the text and software quickly combine learning by reading and learning by doing. The book enables understanding of basic principles of multigrid and local Fourier analysis, and also describes the theory important to those who need to delve deeper into the details of the subject.

The first chapter delivers an explanation of concepts, including Fourier components and multigrid principles. Chapter 2 highlights the basic elements of local Fourier analysis and the limits to this approach. Chapter 3 examines multigrid methods and components, supported by a user-friendly GUI. Chapter 4 provides case studies for two- and three-dimensional problems. Chapters 5 and 6 detail the mathematics embedded within the software system. Chapter 7 presents recent developments and further applications of local Fourier analysis for multigrid methods.

Goong Chen Texas A & M University, College Station, Texas, USA
Zijian Diao University of Illinois, Urbana, Illinois, USA

Mathematical Theory of Quantum Computation

Series: Chapman & Hall/CRC Applied Mathematics & Nonlinear Science

ISBN: 1-58488-389-8
Publication Date: 9/26/2005
Number of Pages: 320

" Offers the most mathematically comprehensive treatment of the subject currently available
" Provides many illustrations of circuits and devices to promote understanding of the concepts
" Includes discussions on quantum circuit design and quantum error correcting codes

The design and construction of the quantum computer is one of the most exciting and challenging goals of the scientific community. This book provides an introductory yet comprehensive treatment of the mathematical theory underlying quantum computation. The author adopts the formal, axiomatic style of stating and proving lemmas, propositions, and theorems, but does so in a way that keeps the treatment self-contained and accessible to a broad, multidisciplinary audience. Numerous diagrams of circuits and devices support the text. Plentiful exercises and examples reinforce the concepts and make this book suitable for graduate-level course work as well as a valuable reference for researchers and practitioners.

Table of Contents

Introduction to Quantum Mechanics and Quantum Computing Devices. Quantum Circuits and Universality of Quantum Gates. Quantum Computing Complexity. Quantum Fourier Transforms. Quantum Search Algorithms. Shoris Quantum Algorithm for Factoring Integers. Quantum Error Correcting Codes.

Wendy L Martinez The Office of Naval Research, Washington, D.C., USA
Angel R Martinez Naval Surface Warfare Center, Norfolk, Virginia, USA

Exploratory Data Analysis with MATLAB

Series: Chapman & Hall/CRC Computer Science & Data Analysis Volume: 5

ISBN: 1-58488-366-9
Publication Date: 11/24/2004
Number of Pages: 424

" Presents MATLAB code for virtually all algorithms covered in the book for better understanding
" Provides pseudo-code to implement the algorithms presented using other software packages
" Includes MATLAB code that is robust in nature to make the book accessible to many more people and useable for several years

Exploratory Data Analysis with MATLAB is the first book to put a computational emphasis on the methods used to visualize and summarize data before making model assumptions to generate hypotheses. The authors use MATLAB code and algorithmic descriptions to provide the user with state-of-the-art techniques for finding patterns and structure in data. They also focus on the computational aspects of these methodologies as opposed to theoretical. Many annotated references to papers and books help to provide the theoretical aspects of the topic. The approach taken by the authors helps to make exploratory data analysis accessible to a wide range of users.