An Introduction to Data Structures and Algorithms

Data structures and algorithms are presented at the college level

in a highly accessible format that presents material with one-page

displays in a way that will appeal to both teachers and students. The

thirteen chapters cover: Models of Computation, Lists, Induction and

Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees,

Sets Over a Small Universe, Graphs, Strings, Discrete Fourier

Transform, Parallel Computation.

Key features: Complicated concepts are expressed clearly in a

single page with minimal notation and without the "clutter" of the

syntax of a particular programming language; algorithms are presented

with self-explanatory "pseudo-code." * Chapters 1-4 focus on

elementary concepts, the exposition unfolding at a slower pace. Sample

exercises with solutions are provided. Sections that may be skipped

for an introductory course are starred. Requires only some basic

mathematics background and some computer programming experience. *

Chapters 5-13 progress at a faster pace. The material is suitable for

undergraduates or first-year graduates who need only review Chapters 1

-4. * This book may be used for a one-semester introductory course

(based on Chapters 1-4 and portions of the chapters on algorithm

design, hashing, and graph algorithms) and for a one-semester advanced

course that starts at Chapter 5. A year-long course may be based on

the entire book. * Sorting, often perceived as rather technical, is

not treated as a separate chapter, but is used in many examples

(including bubble sort, merge sort, tree sort, heap sort, quick sort,

and several parallel algorithms). Also, lower bounds on sorting by

comparisons are included with the presentation of heaps in the context

of lower bounds for comparison-based structures. * Chapter 13 on

parallel models of computation is something of a mini-book itself, and

a good way to end a course. Although it is not clear what parallel