• Title: Algorithms
• Author(s) Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani
• Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (September 13, 2006)
• Paperback: 336 pages
• eBook: PDF Files
• Language: English
• ISBN-10: 0073523402
• ISBN-13: 978-0073523408
• Share This:  

Book Description

This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.

• N/A

• Amazon

• Data Structures and Algorithms
• Computational Complexity
• Operations Research (OR), Linear Programming, Optimization, and Approximation

• Algorithms (Sanjoy Dasgupta, et al)
• The Mirror Site (1) - PDF Files
• The Mirror Site (2) - PDF
• The Mirror Site (3) - PDF
• Book Homepage (Errata, etc.)

• Algorithm Design (Jon Kleinberg, et al)

  This book introduces algorithms by looking at the real-world problems that motivate them. The book teaches a range of design and analysis techniques for problems that arise in computing applications.

• Lecture Notes for the Algorithms (Jeff Erickson)

  This lecture notes uniquely combines rigor and comprehensiveness. It 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.

• Algorithms: Fundamental Techniques (Macneil Shonle, et al)

  The goal of the book is to show you how you can methodically apply different techniques to your own algorithms to make them more efficient. While this book mostly highlights general techniques, some well-known algorithms are also looked at in depth.

• Elementary Algorithms (Xinyu Liu)

  This book doesn't only focus on an imperative (or procedural) approach, but also includes purely functional algorithms and data structures. It teaches you how to think like a programmer - find the practical efficiency algorithms to solve your problems.

• Algorithms and Data Structures (Niklaus Wirth)

  From the inventor of Pascal and Modula-2 comes a new version of Niklaus Wirth's classic work, Algorithms + Data Structure = Programs (PH, l975). It includes new material on sequential structure, searching and priority search trees.

• The Algorithm Design Manual (Steven S. Skiena)

  This book serves as the primary textbook for any algorithm design course while maintaining its status as the premier practical reference guide to algorithms, intended as a manual on algorithm design for both students and computer professionals.

• Introduction to Design Analysis of Algorithms (K. Raghava Rao)

  This book was very useful to easily understand the algorithms. This book is having enough examples on every algorithm. It has written for the sake of students to provide complete knowledge on Algorithms.

• Clever Algorithms: Nature-Inspired Programming Recipes

  The book describes 45 algorithms from the field of Artificial Intelligence. All algorithm descriptions are complete and consistent to ensure that they are accessible, usable and understandable by a wide audience.

• The Design of Approximation Algorithms (D. P. Williamson)

  This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization, etc.

• Planning Algorithms (Steven M. LaValle)

  This is the only book for teaching and referencing of Planning Algorithms in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications and medicine, etc.

• Parallel Algorithms (Henri Casanova, et al)

  Focusing on algorithms for distributed-memory parallel architectures, the book extracts fundamental ideas and algorithmic principles from the mass of parallel algorithm expertise and practical implementations developed over the last few decades.

• Numerical Algorithms: Computer Vision, Machine Learning, etc.

  This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.