Introduction to Algorithms (3rd Edition)

Introduction to AlgorithmsFree download Introduction to Algorithms Third Edition in PDF written by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein and published by The MIT Press.

According to the Authors, ” Before there were computers, there were algorithms. But now that there are computers,there are even more algorithms, and algorithms lie at the heart of computing. This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. We have tried to keep explanations elementary without sacrificing depth of coverage or mathematical rigor.

The text is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Because it discusses engineering issues in algorithm design, as well as mathematical aspects, it is equally well suited for self-study by technical professionals.

In this, the third edition, we have once again updated the entire book. The changes cover a broad spectrum, including new chapters, revised pseudo-code, and a more active writing style.  The magnitude of the changes is on a par with the changes between the first and second editions. As we said about the second-edition changes, depending on how you look at it, the book changed either not much or quite a bit. A quick look at the table of contents shows that most of the second-edition chapters and sections appear in the third edition. We removed two chapters and one section, but we have added three new chapters and two new sections apart from these new chapters.

Here is a summary of the most significant changes for the third edition:

  • We added new chapters on van Emde Boas trees and multi threaded algorithms, and we have broken out material on matrix basics into its own appendix chapter.
  • We revised the chapter on recurrences to more broadly cover the divide-and conquer technique, and its first two sections apply divide-and-conquer to solve two problems. The second section of this chapter presents Strassen’s algorithm for matrix multiplication, which we have moved from the chapter on matrix operations.
  • We removed two chapters that were rarely taught: binomial heaps and sorting networks. One key idea in the sorting networks chapter, the 0-1 principle, appears in this edition within Problem 8-7 as the 0-1 sorting lemma for compare exchange algorithms. The treatment of Fibonacci heaps no longer relies on binomial heaps as a precursor.
  • Based on many requests, we changed the syntax (as it were) of our pseudo-code. We now use “D” to indicate assignment and “==”to test fore quality ,just as C, C++, Java, and Python do. Likewise, we have eliminated the keywords do and the nand adopted “//”as our comment-to-end-of-line symbol. We also now use dot-notation to indicate object attributes. Our pseudo-code remains procedural, rather than object-oriented. In other words, rather than running methods on objects, we simply call procedures, passing objects as parameters.

Table of Contents

  1. Foundations, The Role of Algorithm in Computing
  2. Getting Started
  3. Growth of Functions
  4. Divide and Conquer
  5. Probabilistic Analysis and Randomized Algorithms
  6. Sorting and Order Statistics, Introduction,Heapsort
  7. Quicksort
  8. Sorting in Linear Time
  9. Medians and Order Statistics
  10. Data Structures, Elementary Data Structures
  11. Hash Tables
  12. Binary Search Trees
  13. Red-Black Trees
  14. Augmenting Data Structures
  15. Advanced Design and Analysis Techniques, Dynamic Programming
  16. Greedy Algorithms
  17. Amortized Analysis
  18. Advanced Data Structures, B-Trees
  19. Fibonacci Heaps
  20. Van Emde Boas Trees
  21. Data Structures for Disjoint Sets
  22. Graph Algorithms, Elementary Graph Algorithms
  23. Minimum Spanning Trees
  24. Single Source Shortest Paths
  25. All Pairs Shortest Paths
  26. Maximum Flow
  27. Multi-Threaded Algorithms
  28. Matrix Operations
  29. Linear Programming
  30. Polynomials and the FFT
  31. Number-Theoretic Algorithms
  32. String Matching
  33. Computational Geometry
  34. NP-Completeness
  35. Approximation Algorithms
  36. Appendixes

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