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☆ WHY TO READ THIS BOOK:-
• one of the best book to learn the concepts of competitive programming in python language.
• This book is based upon the book Data Structures and Algorithms in Java by
Goodrich and Tamassia, and the related Data Structures and Algorithms in C++
by Goodrich, Tamassia, and Mount. However, this book is not simply a translation of those other books to Python. In adapting the material for this book, they have significantly redesigned the organization and content of the book as follows:
• The code base has been entirely redesigned to take advantage of the features
of Python, such as use of generators for iterating elements of a collection.
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• Many algorithms that were presented as pseudo-code in the Java and C++ versions are directly presented as complete Python code.
• In general, ADTs are defined to have consistent interface with Python’s built
in data types and those in Python’s collections module.
• Chapter 5 provides an in-depth exploration of the dynamic array-based underpinnings of Python’s built-in list, tuple, and str classes.
• Over 450 illustrations have been created or revised.
• New and revised exercises bring the overall total number to 750.
☆ CHAPTERS IN THIS BOOK ARE:-
1. Python Primer
2. Object-Oriented Programming
3. Algorithm Analysis
4. Recursion
5. Array-Based Sequences
6. Stacks, Queues, and Deques
7. Linked Lists
8. Trees
9. Priority Queues
10. Maps, Hash Tables, and Skip Lists
11. Search Trees
12. Sorting and Selection
13. Text Processing
14. Graph Algorithms
15. Memory Management and B-Trees
A. Character Strings in Python
B. Useful Mathematical Facts
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☆ ABOUT THE AUTHORS:-
Michael Goodrich received his Ph.D. in Computer Science from Purdue University
in 1987. He is currently a Chancellor’s Professor in the Department of Computer
Science at University of California, Irvine. Previously, he was a professor at Johns
Hopkins University. He is a Fulbright Scholar and a Fellow of the American Association for the Advancement of Science (AAAS), Association for Computing Machinery (ACM), and Institute of Electrical and Electronics Engineers (IEEE).
He is a recipient of the IEEE Computer Society Technical Achievement Award,
the ACM Recognition of Service Award, and the Pond Award for Excellence in
Undergraduate Teaching.
Roberto Tamassia received his Ph.D. in Electrical and Computer Engineering
from the University of Illinois at Urbana-Champaign in 1988. He is the Plastech
Professor of Computer Science and the Chair of the Department of Computer Science at Brown University. He is also the Director of Brown’s Center for Geometric Computing. His research interests include information security, cryptography, analysis, design, and implementation of algorithms, graph drawing and computational
geometry.
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He is a Fellow of the American Association for the Advancement of Science (AAAS), Association for Computing Machinery (ACM) and Institute for Electrical and Electronic Engineers (IEEE). He is also a recipient of the Technical Achievement Award from the IEEE Computer Society.
Michael Goldwasser received his Ph.D. in Computer Science from Stanford
University in 1997. He is currently a Professor in the Department of Mathematics
and Computer Science at Saint Louis University and the Director of their Computer Science program. Previously, he was a faculty member in the Department
of Computer Science at Loyola University Chicago. His research interests focus
on the design and implementation of algorithms, having published work involving
approximation algorithms, online computation, computational biology, and computational geometry. He is also active in the computer science education community.
