## 7.7 Comparison of Mergesort, Quicksort, and Heapsort
[Table 7.2](#ch07table02)summarizes our results concerning the three algorithms. Because Heapsort is, on the average, worse than Quicksort in terms of both comparisons of keys and assignments of records, and because Quicksort's extra space usage is minimal, Quicksort is usually preferred to Heapsort. Because our original implementation of Mergesort ([Algorithms 2.2](LiB0016.html#158)and [2.4](LiB0016.html#169))uses an entire additional
array of records, and because Mergesort always does about three times as many assignments of records as Quicksort does on the average, Quicksort is usually preferred to Mergesort even though Quicksort does slightly more comparisons of keys on the average. However, the linked implementation of Mergesort ([Algorithm 7.4](LiB0055.html#661)) eliminates almost all the disadvantages of Mergesort. The only disadvantage remaining is the additional space used for Θ (*n*) extra links.
**![](https://box.kancloud.cn/e95df9d604ab2d89febe370ae4d88fb1_1x1.gif)**Table 7.2: Analysis summary for Θ (*n* lg *n*) sorting algorithms\[[\*](#ftn.ch07table02fnt01)\]Algorithm
Comparisons of Keys
Assignments of Records
Extra Space Usage
Mergesort
*W* (n) = n *lg* n
T (*n*) = 2*n* lg *n*
Θ (*n*) records
([Algorithm 2.4](LiB0016.html#169))
*A (n) = n* lg *n*
Mergesort
*W* (n) = *n*lg*n*
T (*n*) = 0\[[†](#ftn.ch07table02fnt02)\]
Θ (*n*) links
([Algorithm 7.4](LiB0055.html#661))
*A* (n) = *n*lg*n*
Quicksort
*W (n) = n*2/2
Θ (lg *n*) indices
(with improvements)
A (*n*) = 1.38*n* lg *n*
A (*n*) = 0.69*n* lg *n*
Heapsort
*W (n*) = 2*n* lg *n*
W (n) = *n* lg *n*
In-place
A (*n*) = 2*n* lg *n*
A (n) = *n* lg *n*
\[[\*](#ch07table02fnt01)\]Entries are approximate; the average cases for Mergesort and Heapsort are slightly better than the worst cases. tIf it is required that the records be in sorted sequence in contiguous array slots, the worst case is in Θ (*n*).
\[[†](#ch07table02fnt02)\]If it is required that the records be in sorted sequence in contiguous array slots, the worst case is in Θ (*n*).
**![](https://box.kancloud.cn/e95df9d604ab2d89febe370ae4d88fb1_1x1.gif)**
- Table of Contents
- BackCover
- Foundations of Algorithms Using C++ Pseudocode, Third Edition
- Preface
- Chapter Contents
- Pedagogy
- Course Outlines
- Acknowledgments
- Errors
- Chapter 1: Algorithms - Efficiency, Analysis, and Order
- 1.2 The Importance of Developing Efficient Algorithms
- 1.3 Analysis of Algorithms
- 1.4 Order
- 1.5 Outline of This Book
- Exercises
- Chapter 2: Divide-and-Conquer
- 2.1 Binary Search
- 2.2 Mergesort
- 2.3 The Divide-and-Conquer Approach
- 2.4 Quicksort (Partition Exchange Sort)
- 2.5 Strassen's Matrix Multiplication Algorithm
- 2.6 Arithmetic with Large Integers
- 2.7 Determining Thresholds
- 2.8 When Not to Use Divide-and-Conquer
- Exercises
- Chapter 3: Dynamic Programming
- 3.1 The Binomial Coefficient
- 3.2 Floyd's Algorithm for Shortest Paths
- 3.3 Dynamic Programming and Optimization Problems
- 3.4 Chained Matrix Multiplication
- 3.5 Optimal Binary Search Trees
- 3.6 The Traveling Salesperson Problem
- Exercises
- Chapter 4: The Greedy Approach
- 4.1 Minimum Spanning Trees
- 4.2 Dijkstra's Algorithm for Single-Source Shortest Paths
- 4.3 Scheduling
- 4.4 Huffman Code
- 4.5 The Greedy Approach versus Dynamic Programming: The Knapsack Problem
- Exercises
- Chapter 5: Backtracking
- 5.2 The n-Queens Problem
- 5.3 Using a Monte Carlo Algorithm to Estimate the Efficiency of a Backtracking Algorithm
- 5.4 The Sum-of-Subsets Problem
- 5.5 Graph Coloring
- 5.6 The Hamiltonian Circuits Problem
- 5.7 The 0-1 Knapsack Problem
- Exercises
- Chapter 6: Branch-and-Bound
- 6.1 Illustrating Branch-and-Bound with the 0 - 1 Knapsack problem
- 6.2 The Traveling Salesperson Problem
- 6.3 Abductive Inference (Diagnosis)
- Exercises
- Chapter 7: Introduction to Computational Complexity - The Sorting Problem
- 7.2 Insertion Sort and Selection Sort
- 7.3 Lower Bounds for Algorithms that Remove at Most One Inversion per Comparison
- 7.4 Mergesort Revisited
- 7.5 Quicksort Revisited
- 7.6 Heapsort
- 7.6.1 Heaps and Basic Heap Routines
- 7.6.2 An Implementation of Heapsort
- 7.7 Comparison of Mergesort, Quicksort, and Heapsort
- 7.8 Lower Bounds for Sorting Only by Comparison of Keys
- 7.8.1 Decision Trees for Sorting Algorithms
- 7.8.2 Lower Bounds for Worst-Case Behavior
- 7.8.3 Lower Bounds for Average-Case Behavior
- 7.9 Sorting by Distribution (Radix Sort)
- Exercises
- Chapter 8: More Computational Complexity - The Searching Problem
- 8.1 Lower Bounds for Searching Only by Comparisons of Keys
- 8.2 Interpolation Search
- 8.3 Searching in Trees
- 8.4 Hashing
- 8.5 The Selection Problem: Introduction to Adversary Arguments
- Exercises
- Chapter 9: Computational Complexity and Interactability - An Introduction to the Theory of NP
- 9.2 Input Size Revisited
- 9.3 The Three General Problem Categories
- 9.4 The Theory of NP
- 9.5 Handling NP-Hard Problems
- Exercises
- Chapter 10: Number-Theoretic Algorithms
- 10.1 Number Theory Review
- 10.2 Computing the Greatest Common Divisor
- 10.3 Modular Arithmetic Review
- 10.4 Solving Modular Linear Equations
- 10.5 Computing Modular Powers
- 10.6 Finding Large Prime Numbers
- 10.7 The RSA Public-Key Cryptosystem
- Exercises
- Chapter 11: Introduction to Parallel Algorithms
- 11.1 Parallel Architectures
- 11.2 The PRAM Model
- Exercises
- Appendix A: Review of Necessary Mathematics
- A.2 Functions
- A.3 Mathematical Induction
- A.4 Theorems and Lemmas
- A.5 Logarithms
- A.6 Sets
- A.7 Permutations and Combinations
- A.8 Probability
- Exercises
- Appendix B: Solving Recurrence Equations - With Applications to Analysis of Recursive Algorithms
- B.2 Solving Recurrences Using the Characteristic Equation
- B.3 Solving Recurrences by Substitution
- B.4 Extending Results for n, a Power of a Positive Constant b, to n in General
- B.5 Proofs of Theorems
- Exercises
- Appendix C: Data Structures for Disjoint Sets
- References
- Index
- Index_B
- Index_C
- Index_D
- Index_E
- Index_F
- Index_G
- Index_H
- Index_I
- Index_J
- Index_K
- Index_L
- Index_M
- Index_N
- Index_O
- Index_P
- Index_Q
- Index_R
- Index_S
- Index_T
- Index_U
- Index_V
- Index_W-X
- Index_Y
- Index_Z
- List of Figures
- List of Tables
- List of Algorithms, Examples, and Theorems
- List of Sidebars