Buch, Englisch, 272 Seiten, Format (B × H): 242 mm x 165 mm, Gewicht: 522 g
Buch, Englisch, 272 Seiten, Format (B × H): 242 mm x 165 mm, Gewicht: 522 g
ISBN: 978-1-138-40225-6
Verlag: Taylor & Francis Ltd
Until now, no other book examined the gap between the theory of algorithms and the production of software programs. Focusing on practical issues, A Programmer's Companion to Algorithm Analysis carefully details the transition from the design and analysis of an algorithm to the resulting software program.
Consisting of two main complementary parts, the book emphasizes the concrete aspects of translating an algorithm into software that should perform based on what the algorithm analysis indicated. In the first part, the author describes the idealized universe that algorithm designers inhabit while the second part outlines how this ideal can be adapted to the real world of programming. The book explores analysis techniques, including crossover points, the influence of the memory hierarchy, implications of programming language aspects, such as recursion, and problems arising from excessively high computational complexities of solution methods. It concludes with four appendices that discuss basic algorithms; memory hierarchy, virtual memory management, optimizing compilers, and garbage collection; NP-completeness and higher complexity classes; and undecidability in practical terms.
Applying the theory of algorithms to the production of software, A Programmer's Companion to Algorithm Analysis fulfills the needs of software programmers and developers as well as students by showing that with the correct algorithm, you can achieve a functional software program.
Zielgruppe
Professional
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
A Taxonomy of Algorithmic Complexity. Fundamental Assumptions Underlying Algorithmic Complexity. Examples of Complexity Analysis. Sources of Disappointments. Implications of Nonuniform Memory for Software. Implications of Compiler and Systems Issues for Software. Implicit Assumptions. Implications of the Finiteness of the Representation of Numbers. Asymptotic Complexities and the Selection of Algorithms. Infeasibility and Undecidability: Implications for Software Development. Appendix I: Algorithms Every Programmer Should Know. Appendix II: Overview of Systems Implicated in Program Analysis. Appendix III: NP-Completeness and Higher Complexity Classes. Appendix IV: Review of Undecidability.Bibliography. Index.
