Analysis for computer scientists : foundations, methods, and algorithms / Michael Oberguggenberger, Alexander Ostermann ; translated in collaboration with Elisabeth Bradley

By: Oberguggenberger, Michael, 1953- [author]Contributor(s): Ostermann, Alexander [author] | Bradley, Elisabeth [translator] | Ohio Library and Information NetworkMaterial type: TextTextSeries: Undergraduate topics in computer sciencePublisher: Cham, Switzerland : Springer, 2018Edition: Second editionDescription: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319911557; 3319911554Subject(s): Computer science -- MathematicsGenre/Form: Electronic books Additional physical formats: Print version:: No titleDDC classification: 004.0151 LOC classification: QA76.9.M35Online resources: SpringerLink Connect to resource | SpringerLink Connect to resource | SpringerLink Connect to resource (off-campus) Summary: This 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. Topics and features : 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 (NEW); Contains experiments, exercises, definitions, and propositions throughout the text; Supplies programming examples in Python, in addition to MATLAB (NEW); Provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material
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Previous edition: 2011

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This 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. Topics and features : 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 (NEW); Contains experiments, exercises, definitions, and propositions throughout the text; Supplies programming examples in Python, in addition to MATLAB (NEW); Provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material

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