Introduction to Algorithms – Cormen, Leiserson, Revest, Stein – 2nd Edition


The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition, this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects.
In its new edition, Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book’s original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity, and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.

As in the classic first edition, this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further, the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.

Each chapter presents an algorithm, a design technique, an application area, or a related topic. The chapters are not dependent on one another, so the instructor can organize his or her use of the book in the way that best suits the course’s needs.

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  • I Foundations

    1 The Role of Algorithms in Computing
    2 Getting Started
    3 Growth of Functions
    4 Recurrences
    5 Probabilistic Analysis and Randomized Algorithms

    II Sorting and Order Statistics

    6 Heapsort
    7 Quicksort
    8 Sorting in Linear Time
    9 Medians and Order Statistics

    III Data Structures

    10 Elementary Data Structures
    11 Hash Tables
    12 Binary Search Trees
    13 Red-Black Trees
    14 Augmenting Data Structures
    15 Dynamic Programming
    16 Greedy Algorithms
    17 Amortized Analysis

    V Advanced Data Structures

    18 B-Trees
    19 Binomial Heaps
    20 Fibonacci Heaps
    21 Data Structures for Disjoint Sets

    VI Graph Algorithms

    22 Elementary Graph Algorithms
    23 Minimum Spanning Trees
    24 Single-Source Shortest Paths
    25 All-Pairs Shortest Paths
    26 Maximum Flow

    VII Selected Topics

    27 Sorting Networks
    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
  • Citation

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