Analysis for Computer Scientists: Foundations, Methods, and Algorithms

Mathematics and mathematical modelling are of central importance in computer science, and therefore it is vital that computer scientists are aware of the latest concepts and techniques.

This concise and easy-to-read textbook/reference presents an algorithmic approach to mathematical analysis, with a focus on modelling and on the applications of analysis. Fully integrating mathematical software into the text as an important component of analysis, the book makes thorough use of examples and explanations using MATLAB, Maple, and Java applets. Mathematical theory is described alongside the basic concepts and methods of numerical analysis, supported by computer experiments and programming exercises, and an extensive use of figure illustrations.

Topics and features: thoroughly describes the essential concepts of analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives and antiderivatives, definite integrals and double integrals, and curves
provides summaries and exercises in each chapter, as well as computer experiments
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 definitions, propositions and examples throughout the text, together with a list of relevant textbooks and references for further reading
supplementary software can be downloaded from the book's webpage at www.springer.com.

This textbook is essential for undergraduate students in Computer Science. Written to specifically address the needs of computer scientists and researchers, it will also serve professionals looking to bolster their knowledge in such fundamentals extremely well.