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Basic Category Theory for Computer Scientists

Basic Category Theory for Computer Scientists
Author: Benjamin C. Pierce
Publisher: MIT Press
Total Pages: 117
Release: 1991-08-07
Genre: Computers
ISBN: 0262326450

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Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Category theory is a branch of pure mathematics that is becoming an increasingly important tool in theoretical computer science, especially in programming language semantics, domain theory, and concurrency, where it is already a standard language of discourse. Assuming a minimum of mathematical preparation, Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Four case studies illustrate applications of category theory to programming language design, semantics, and the solution of recursive domain equations. A brief literature survey offers suggestions for further study in more advanced texts. Contents Tutorial • Applications • Further Reading


Scientific Computing

Scientific Computing
Author: Timo Heister
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 184
Release: 2023-04-03
Genre: Computers
ISBN: 3110988453

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Scientific Computing for Scientists and Engineers is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing. Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations? Scientific Computing for Scientists and Engineers covers: An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems; Scientific computing fundamentals like floating point representation of numbers and convergence; Analysis of accuracy and efficiency; Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems; Exercises to reinforce all topics.


Introduction to High Performance Computing for Scientists and Engineers

Introduction to High Performance Computing for Scientists and Engineers
Author: Georg Hager
Publisher: CRC Press
Total Pages: 350
Release: 2010-07-02
Genre: Computers
ISBN: 1439811938

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Written by high performance computing (HPC) experts, Introduction to High Performance Computing for Scientists and Engineers provides a solid introduction to current mainstream computer architecture, dominant parallel programming models, and useful optimization strategies for scientific HPC. From working in a scientific computing center, the author


The Science of Computing

The Science of Computing
Author: Matti Tedre
Publisher: CRC Press
Total Pages: 294
Release: 2014-12-03
Genre: Computers
ISBN: 1482217694

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The identity of computing has been fiercely debated throughout its short history. Why is it still so hard to define computing as an academic discipline? Is computing a scientific, mathematical, or engineering discipline? By describing the mathematical, engineering, and scientific traditions of computing, The Science of Computing: Shaping a Discipline presents a rich picture of computing from the viewpoints of the field’s champions. The book helps readers understand the debates about computing as a discipline. It explains the context of computing’s central debates and portrays a broad perspective of the discipline. The book first looks at computing as a formal, theoretical discipline that is in many ways similar to mathematics, yet different in crucial ways. It traces a number of discussions about the theoretical nature of computing from the field’s intellectual origins in mathematical logic to modern views of the role of theory in computing. The book then explores the debates about computing as an engineering discipline, from the central technical innovations to the birth of the modern technical paradigm of computing to computing’s arrival as a new technical profession to software engineering gradually becoming an academic discipline. It presents arguments for and against the view of computing as engineering within the context of software production and analyzes the clash between the theoretical and practical mindsets. The book concludes with the view of computing as a science in its own right—not just as a tool for other sciences. It covers the early identity debates of computing, various views of computing as a science, and some famous characterizations of the discipline. It also addresses the experimental computer science debate, the view of computing as a natural science, and the algorithmization of sciences.


Introduction to the Tools of Scientific Computing

Introduction to the Tools of Scientific Computing
Author: Einar Smith
Publisher: Springer Nature
Total Pages: 344
Release: 2020-12-02
Genre: Mathematics
ISBN: 3030608085

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The book provides an introduction to common programming tools and methods in numerical mathematics and scientific computing. Unlike widely used standard approaches, it does not focus on any particular language but aims to explain the key underlying concepts. In general, new concepts are first introduced in the particularly user-friendly Python language and then transferred and expanded in various scientific programming environments from C / C ++, Julia and MATLAB to Maple. This includes different approaches to distributed computing. The fact that different languages are studied and compared also makes the book useful for mathematicians and practitioners trying to decide which programming language to use for which purposes.


Computing for Scientists and Engineers

Computing for Scientists and Engineers
Author: William J. Thompson
Publisher: Wiley-VCH
Total Pages: 472
Release: 1992-10-30
Genre: Mathematics
ISBN:

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Topics are divided between review material on the mathematics background; numerical-analysis methods such as differentiation, integration, the solution of differential equations from engineering, life and physical sciences; data-analysis applications including least-squares fitting, splines and Fourier expansions. Unique in its project orientation, it features a vast amount of exercises with emphasis on realistic examples from current applications.


C++ and Object-Oriented Numeric Computing for Scientists and Engineers

C++ and Object-Oriented Numeric Computing for Scientists and Engineers
Author: Daoqi Yang
Publisher: Springer Science & Business Media
Total Pages: 452
Release: 2011-06-28
Genre: Computers
ISBN: 1461301890

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This book is an easy, concise but fairly complete introduction to ISO/ANSI C++ with special emphasis on object-oriented numeric computation. A user-defined numeric linear algebra library accompanies the book and can be downloaded from the web.


Computer Architecture for Scientists

Computer Architecture for Scientists
Author: Andrew A. Chien
Publisher: Cambridge University Press
Total Pages: 266
Release: 2022-03-10
Genre: Computers
ISBN: 1009008382

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The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerators that have become increasingly important for mainstream applications.


Scientific Computing

Scientific Computing
Author: Michael T. Heath
Publisher: SIAM
Total Pages: 567
Release: 2018-11-14
Genre: Science
ISBN: 1611975573

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This book differs from traditional numerical analysis texts in that it focuses on the motivation and ideas behind the algorithms presented rather than on detailed analyses of them. It presents a broad overview of methods and software for solving mathematical problems arising in computational modeling and data analysis, including proper problem formulation, selection of effective solution algorithms, and interpretation of results.? In the 20 years since its original publication, the modern, fundamental perspective of this book has aged well, and it continues to be used in the classroom. This Classics edition has been updated to include pointers to Python software and the Chebfun package, expansions on barycentric formulation for Lagrange polynomial interpretation and stochastic methods, and the availability of about 100 interactive educational modules that dynamically illustrate the concepts and algorithms in the book. Scientific Computing: An Introductory Survey, Second Edition is intended as both a textbook and a reference for computationally oriented disciplines that need to solve mathematical problems.


Computing for Scientists

Computing for Scientists
Author: R. J. Barlow
Publisher: John Wiley & Sons
Total Pages: 300
Release: 1998-09-16
Genre: Science
ISBN: 9780471955962

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Strategien zur Lösung wissenschaftlicher Probleme mittels Fortran 90 und C++ sind Thema dieses Buches. Behandelt werden Fragestellungen, denen sich Naturwissenschaftler im Alltag häufig gegenübersehen, wie Simulationen, Graphik, Datenanalyse und die Manipulation von Datenstrukturen. Den Autoren kommt es nicht darauf an, zu zeigen, wie man ein Problem codiert - sie zielen eher auf die Vermittlung allgemeingültiger Prinzipien ab. Mit zahlreichen Beispielen. (8/98)