Analysis for Computer Scientists
Foundations, Methods, and Algorithms
by Michael Oberguggenberger, Alexander Ostermann
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Describes the fundamental concepts in analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives, integrals, and curves; Discusses important applications and advanced topics, such as fractals and L-systems, numerical integration, linear regression, and differential equations; Presents tools from vector and matrix algebra in the appendices, together with further information on continuity; Includes added material on hyperbolic functions, curves and surfaces in space, second-order differential equations, and the pendulum equation; Contains experiments, exercises, definitions, and propositions throughout the text; Supplies programming examples in Python, in addition to MATLAB; Provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material.
Addressing the core needs of computer science students and researchers, this clearly written textbook is an essential resource for undergraduate-level courses on numerical analysis, and an ideal self-study tool for professionals seeking to enhance their analysis skills. 
Book Description
This easy-to-follow textbook/reference presents a concise introduction to mathematical analysis from an algorithmic point of view, with a particular focus on applications of analysis and aspects of mathematical modelling. The text describes the mathematical theory alongside the basic concepts and methods of numerical analysis, enriched by computer experiments using MATLAB, Python, Maple, and Java applets. This fully updated and expanded new edition also features an even greater number of programming exercises.Describes the fundamental concepts in analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives, integrals, and curves; Discusses important applications and advanced topics, such as fractals and L-systems, numerical integration, linear regression, and differential equations; Presents tools from vector and matrix algebra in the appendices, together with further information on continuity; Includes added material on hyperbolic functions, curves and surfaces in space, second-order differential equations, and the pendulum equation; Contains experiments, exercises, definitions, and propositions throughout the text; Supplies programming examples in Python, in addition to MATLAB; Provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material.
Addressing the core needs of computer science students and researchers, this clearly written textbook is an essential resource for undergraduate-level courses on numerical analysis, and an ideal self-study tool for professionals seeking to enhance their analysis skills.
This open book is licensed under a Creative Commons License (CC BY). You can download Analysis for Computer Scientists ebook for free in PDF format (4.6 MB).
Book Details
Title
Analysis for Computer Scientists
Subject
Computer Science
Publisher
Springer
Published
2018
Pages
372
Edition
2
Language
English
ISBN13
9783319911540
ISBN10
3319911546
ISBN13 Digital
9783319911557
ISBN10 Digital
3319911554
PDF Size
4.6 MB
License
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