Data Structures and Algorithms: Annotated Reference with Examples
by Granville Barnett, Luca Del Tongo
Publisher: DotNetSlackers 2008
Number of pages: 112
This book provides implementations of common and uncommon algorithms in pseudocode which is language independent and provides for easy porting to most imperative programming languages. We assume that the reader is familiar with the following: (1) Big Oh notation; (2) An imperative programming language; (3) Object oriented concepts.
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by Niklaus Wirth - Prentice Hall
The book treats practically important algorithms and data structures. It starts with a chapter on data structure, then it treats sorting algorithms, concentrates on several examples of recursion, and deals with dynamic data structures.
by Anil K. Jain, Richard C. Dubes - Prentice Hall
The book is useful for scientists who gather data and seek tools for analyzing and interpreting data. It will be a reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis.
by Wolfgang Merkle - ESSLLI
The first part of the course gives an introduction to randomized algorithms and to standard techniques for their derandomization. The second part presents applications of the probabilistic method to the construction of logical models.
by Sean Luke
This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course.