Introduction To Algorithms
The first edition won the award for Best 1990 Professional and Scholarly Book in Computer Science and Data Processing by the Association of American Publishers.
There are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms combines rigor and comprehensiveness.
The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.
The first edition became the standard reference for professionals and a widely used text in universities worldwide. The second edition features new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming, as well as extensive revisions to virtually every section of the book. In a subtle but important change, loop invariants are introduced early and used throughout the text to prove algorithm correctness. Without changing the mathematical and analytic focus, the authors have moved much of the mathematical foundations material from Part I to an appendix and have included additional motivational material at the beginning.
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Bewertungen von Nutzern
Review: Introduction to AlgorithmsNutzerbericht - Dimitar Dinchev - Goodreads
It's a great book to use if you are trying to teach algorithms or as reference. However if you aim to learn algorithms for first time, go with one of Sedgwick's Algorithms books. Vollständige Rezension lesen
Review: Introduction to AlgorithmsNutzerbericht - Kaung Htet Zaw - Goodreads
One of the best algorithm textbooks out there. Always my go-to book for algorithm reference. Vollständige Rezension lesen
Getting Started 75
Growth of Functions
Probabilistic Analysis and Randomized Algorithms
Augmenting Data Structures
Minimum Spanning Trees
SingleSource Shortest Paths
Polynomials and the FFT 522
Multi-Objective Optimization Using Evolutionary Algorithms
Eingeschränkte Leseprobe - 2001