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Handbook of Computational Group Theory

Handbook of Computational Group Theory

The origins of computation group theory (CGT) date back to the late 19th and early 20th centuries. Since then the field has flourished particularly during the past 30 to 40 years and today it remains a lively and active branch of mathematics. The Handbook of Computational Group Theory offers the first complete treatment of all the fundamental methods and algorithms in CGT presented at a level accessible even to advanced undergraduate students. It develops the theory of algorithms in full detail and highlights the connections between the different aspects of CGT and other areas of computer algebra. While acknowledging the importance of the complexity analysis of CGT algorithms the authors' primary focus is on algorithms that perform well in practice rather than on those with the best theoretical complexity. Throughout the book applications of all the key topics and algorithms to areas both within and outside of mathematics demonstrate how CGT fits into the wider world of mathematics and science. The authors include detailed pseudocode for all of the fundamental algorithms and provide detailed worked examples that bring the theorems and algorithms to life.

GBP 44.99
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Basic Experimental Strategies and Data Analysis for Science and Engineering

Basic Experimental Strategies and Data Analysis for Science and Engineering

Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform when to quit and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments give the greatest degree of reliability to their conclusions and keep the risk of overlooking something of practical importance to a minimum. Features:Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledgeFeatures a review of the necessary statistical prerequisitesPresents a set of tables that allow readers to quickly access various experimental designsIncludes a roadmap for where and when to use various experimental design strategiesShows compelling examples of each method discussedIllustrates how to reproduce results using several popular software packages on a supplementary websiteFollowing the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand follow the design to conduct the experiments and analyze and interpret the resulting data. John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students. | Basic Experimental Strategies and Data Analysis for Science and Engineering

GBP 44.99
1

Exercises in Programming Style

Exercises in Programming Style

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as The best programming book of the decade. This new edition retains the same presentation but has been upgraded to Python 3 and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical basic function composition objects and object interactions reflection and metaprogramming adversity data-centric concurrency interactivity and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style next shows an example program and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

GBP 35.99
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Discrete Mathematics for Computer Science An Example-Based Introduction

Discrete Mathematics for Computer Science An Example-Based Introduction

Discrete Mathematics for Computer Science: An Example-Based Introduction is intended for a first- or second-year discrete mathematics course for computer science majors. It covers many important mathematical topics essential for future computer science majors such as algorithms number representations logic set theory Boolean algebra functions combinatorics algorithmic complexity graphs and trees. Features Designed to be especially useful for courses at the community-college level Ideal as a first- or second-year textbook for computer science majors or as a general introduction to discrete mathematics Written to be accessible to those with a limited mathematics background and to aid with the transition to abstract thinking Filled with over 200 worked examples boxed for easy reference and over 200 practice problems with answers Contains approximately 40 simple algorithms to aid students in becoming proficient with algorithm control structures and pseudocode Includes an appendix on basic circuit design which provides a real-world motivational example for computer science majors by drawing on multiple topics covered in the book to design a circuit that adds two eight-digit binary numbers Jon Pierre Fortney graduated from the University of Pennsylvania in 1996 with a BA in Mathematics and Actuarial Science and a BSE in Chemical Engineering. Prior to returning to graduate school he worked as both an environmental engineer and as an actuarial analyst. He graduated from Arizona State University in 2008 with a PhD in Mathematics specializing in Geometric Mechanics. Since 2012 he has worked at Zayed University in Dubai. This is his second mathematics textbook. | Discrete Mathematics for Computer Science An Example-Based Introduction

GBP 48.99
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Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Whereas many partial solutions and sketches for the odd-numbered exercises appear in the book the Student Solutions Manual written by the author has comprehensive solutions for all odd-numbered exercises and large number of even-numbered exercises. This Manual also offers many alternative solutions to those appearing in the text. These will provide the student with a better understanding of the material. This is the only available student solutions manual prepared by the author of Contemporary Abstract Algebra Tenth Edition and is designed to supplement that text. Table of Contents Integers and Equivalence Relations0. Preliminaries Groups1. Introduction to Groups 2. Groups 3. Finite Groups; Subgroups 4. Cyclic Groups 5. Permutation Groups 6. Isomorphisms 7. Cosets and Lagrange's Theorem 8. External Direct Products 9. Normal Subgroups and Factor Groups 10. Group Homomorphisms 11. Fundamental Theorem of Finite Abelian Groups Rings12. Introduction to Rings 13. Integral Domains14. Ideals and Factor Rings 15. Ring Homomorphisms 16. Polynomial Rings 17. Factorization of Polynomials 18. Divisibility in Integral Domains FieldsFields19. Extension Fields 20. Algebraic Extensions21. Finite Fields 22. Geometric Constructions Special Topics23. Sylow Theorems 24. Finite Simple Groups 25. Generators and Relations 26. Symmetry Groups 27. Symmetry and Counting 28. Cayley Digraphs of Groups 29. Introduction to Algebraic Coding Theory 30. An Introduction to Galois Theory 31. Cyclotomic Extensions Biography Joseph A. Gallian earned his PhD from Notre Dame. In addition to receiving numerous national awards for his teaching and exposition he has served terms as the Second Vice President and the President of the MAA. He has served on 40 national committees chairing ten of them. He has published over 100 articles and authored six books. Numerous articles about his work have appeared in the national news outlets including the New York Times the Washington Post the Boston Globe and Newsweek among many others. | Student Solutions Manual for Gallian's Contemporary Abstract Algebra

GBP 44.99
1

Multiple Imputation of Missing Data in Practice Basic Theory and Analysis Strategies

Multiple Imputation of Missing Data in Practice Basic Theory and Analysis Strategies

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile popular and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e. g. cross-sectional data longitudinal data complex surveys survival data studies subject to measurement error etc. ) are used to demonstrate the methods. In order for readers not only to know how to use the methods but understand why multiple imputation works and how to choose appropriate methods simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github. com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e. g. univariate and multivariate missing data problems missing data in survival analysis longitudinal data complex surveys etc. ) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples illustrative datasets and sample programming code from popular statistical packages (e. g. SAS R WinBUGS) are included in the book. For others they are available at a github site (https://github. com/he-zhang-hsu/multiple_imputation_book) | Multiple Imputation of Missing Data in Practice Basic Theory and Analysis Strategies

GBP 82.99
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