Introduction to the Design and Analysis of Algorithms, 3rd edition

• New to the Third Edition
• Preface

1. Introduction
   • 1.1 What Is an Algorithm?
     • Exercises 1.1
   • 1.2 Fundamentals of Algorithmic Problem Solving
     • Understanding the Problem
     • Ascertaining the Capabilities of the Computational Device
     • Choosing between Exact and Approximate Problem Solving
     • Algorithm Design Techniques
     • Designing an Algorithm and Data Structures
     • Methods of Specifying an Algorithm
     • Proving an Algorithm’s Correctness
     • Analyzing an Algorithm
     • Coding an Algorithm
     • Exercises 1.2
   • 1.3 Important Problem Types
     • Sorting
     • Searching
     • String Processing
     • Graph Problems
     • Combinatorial Problems
     • Geometric Problems
     • Numerical Problems
     • Exercises 1.3
   • 1.4 Fundamental Data Structures
     • Linear Data Structures
     • Graphs
     • Trees
     • Sets and Dictionaries
     • Exercises 1.4
   • Summary
2. Fundamentals of the Analysis of Algorithm Efficiency
   • 2.1 The Analysis Framework
     • Measuring an Input’s Size
     • Units for Measuring Running Time
     • Orders of Growth
     • Worst-Case, Best-Case, and Average-Case Efficiencies
     • Recapitulation of the Analysis Framework
     • Exercises 2.1
   • 2.2 Asymptotic Notations and Basic Efficiency Classes
     • Informal Introduction
     • O-notation
     • -notation
     • -notation
     • Useful Property Involving the Asymptotic Notations
     • Using Limits for Comparing Orders of Growth
     • Basic Efficiency Classes
     • Exercises 2.2
   • 2.3 Mathematical Analysis of Nonrecursive Algorithms
     • Exercises 2.3
   • 2.4 Mathematical Analysis of Recursive Algorithms
     • Exercises 2.4
   • 2.5 Example: Computing the nth Fibonacci Number
     • Exercises 2.5
   • 2.6 Empirical Analysis of Algorithms
     • Exercises 2.6
   • 2.7 Algorithm Visualization
   • Summary
3. Brute Force and Exhaustive Search
   • 3.1 Selection Sort and Bubble Sort
     • Selection Sort
     • Bubble Sort
     • Exercises 3.1
   • 3.2 Sequential Search and Brute-Force String Matching
     • Sequential Search
     • Brute-Force String Matching
     • Exercises 3.2
   • 3.3 Closest-Pair and Convex-Hull Problems by Brute Force
     • Closest-Pair Problem
     • Convex-Hull Problem
     • Exercises 3.3
   • 3.4 Exhaustive Search
     • Traveling Salesman Problem
     • Knapsack Problem
     • Assignment Problem
     • Exercises 3.4
   • 3.5 Depth-First Search and Breadth-First Search
     • Depth-First Search
     • Breadth-First Search
     • Exercises 3.5
   • Summary
4. Decrease-and-Conquer
   • 4.1 Insertion Sort
     • Exercises 4.1
   • 4.2 Topological Sorting
     • Exercises 4.2
   • 4.3 Algorithms for Generating Combinatorial Objects
     • Generating Permutations
     • Generating Subsets
     • Exercises 4.3
   • 4.4 Decrease-by-a-Constant-Factor Algorithms
     • Binary Search
     • Fake-Coin Problem
     • Russian Peasant Multiplication
     • Josephus Problem
     • Exercises 4.4
   • 4.5 Variable-Size-Decrease Algorithms
     • Computing a Median and the Selection Problem
     • Interpolation Search
     • Searching and Insertion in a Binary Search Tree
     • The Game of Nim
     • Exercises 4.5
   • Summary
5. Divide-and-Conquer
   • 5.1 Mergesort
     • Exercises 5.1
   • 5.2 Quicksort
     • Exercises 5.2
   • 5.3 Binary Tree Traversals and Related Properties
     • Exercises 5.3
   • 5.4 Multiplication of Large Integers and Strassen’s Matrix Multiplication
     • Multiplication of Large Integers
     • Strassen’s Matrix Multiplication
     • Exercises 5.4
   • 5.5 The Closest-Pair and Convex-Hull Problems
     • by Divide-and-Conquer
     • The Closest-Pair Problem
     • Convex-Hull Problem
     • Exercises 5.5
   • Summary
6. Transform-and-Conquer
   • 6.1 Presorting
     • Exercises 6.1
   • 6.2 Gaussian Elimination
     • LU Decomposition
     • Computing a Matrix Inverse
     • Computing a Determinant
     • Exercises 6.2
   • 6.3 Balanced Search Trees
     • AVL Trees
     • 2-3 Trees
     • Exercises 6.3
   • 6.4 Heaps and Heapsort
     • Notion of the Heap
     • Heapsort
     • Exercises 6.4
   • 6.5 Horner’s Rule and Binary Exponentiation
     • Horner’s Rule
     • Binary Exponentiation
     • Exercises 6.5
   • 6.6 Problem Reduction
     • Computing the Least Common Multiple
     • Counting Paths in a Graph
     • Reduction of Optimization Problems
     • Linear Programming
     • Reduction to Graph Problems
     • Exercises 6.6
   • Summary
7. Space and Time Trade-Offs
   • 7.1 Sorting by Counting
     • Exercises 7.1
   • 7.2 Input Enhancement in String Matching
     • Horspool’s Algorithm
     • Boyer-Moore Algorithm
     • Exercises 7.2
   • 7.3 Hashing
     • Open Hashing (Separate Chaining)
     • Closed Hashing (Open Addressing)
     • Exercises 7.3
   • 7.4 B-Trees
     • Exercises 7.4
   • Summary
8. Dynamic Programming
   • 8.1 Three Basic Examples
     • Exercises 8.1
   • 8.2 The Knapsack Problem and Memory Functions
     • Memory Functions
     • Exercises 8.2
   • 8.3 Optimal Binary Search Trees
     • Exercises 8.3
   • 8.4 Warshall’s and Floyd’s Algorithms
     • Warshall’s Algorithm
     • Floyd’s Algorithm for the All-Pairs Shortest-Paths Problem
     • Exercises 8.4
   • Summary
9. Greedy Technique
   • 9.1 Prim’s Algorithm
     • Exercises 9.1
   • 9.2 Kruskal’s Algorithm
     • Disjoint Subsets and Union-Find Algorithms
     • Exercises 9.2
   • 9.3 Dijkstra’s Algorithm
     • Exercises 9.3
   • 9.4 Huffman Trees and Codes
     • Exercises 9.4
   • Summary
10. Iterative Improvement
    • 10.1 The Simplex Method
      • Geometric Interpretation of Linear Programming
      • An Outline of the Simplex Method
      • Further Notes on the Simplex Method
      • Exercises 10.1
    • 10.2 The Maximum-Flow Problem
      • Exercises 10.2
    • 10.3 Maximum Matching in Bipartite Graphs
      • Exercises 10.3
    • 10.4 The Stable Marriage Problem
      • Exercises 10.4
    • Summary
11. Limitations of Algorithm Power
    • 11.1 Lower-Bound Arguments
      • Trivial Lower Bounds
      • Information-Theoretic Arguments
      • Adversary Arguments
      • Problem Reduction
      • Exercises 11.1
    • 11.2 Decision Trees
      • Decision Trees for Sorting
      • Decision Trees for Searching a Sorted Array
      • Exercises 11.2
    • 11.3 P, NP, and NP-Complete Problems
      • P and NP Problems
      • NP-Complete Problems
      • Exercises 11.3
    • 11.4 Challenges of Numerical Algorithms
      • Exercises 11.4
    • Summary
12. Coping with the Limitations of Algorithm Power
    • 12.1 Backtracking
      • n-Queens Problem
      • Hamiltonian Circuit Problem
      • Subset-Sum Problem
      • General Remarks
      • Exercises 12.1
    • 12.2 Branch-and-Bound
      • Assignment Problem
      • Knapsack Problem
      • Traveling Salesman Problem
      • Exercises 12.2
    • 12.3 Approximation Algorithms for NP-Hard Problems
      • Approximation Algorithms for the Traveling Salesman Problem
      • Approximation Algorithms for the Knapsack Problem
      • Exercises 12.3
    • 12.4 Algorithms for Solving Nonlinear Equations
      • Bisection Method
      • Method of False Position
      • Newton’s Method
      • Exercises 12.4
    • Summary
    • Epilogue

APPENDIX A

• Useful Formulas for the Analysis of Algorithms
• Properties of Logarithms
• Combinatorics
• Important Summation Formulas
• Sum Manipulation Rules
• Approximation of a Sum by a Definite Integral
• Floor and Ceiling Formulas
• Miscellaneous

APPENDIX B

• Short Tutorial on Recurrence Relations
• Sequences and Recurrence Relations
• Methods for Solving Recurrence Relations
• Common Recurrence Types in Algorithm Analysis

References

Hints to Exercises

Index