Mark Allen Weiss’ innovative approach to algorithms and data structures teaches the simultaneous development of sound analytical and programming skills for the advanced data structures course. Readers learn how to reduce time constraints and develop programs efficiently by analyzing the feasibility of an algorithm before it is coded.
The C++ language is brought up-to-date and simplified, and the Standard Template Library is now fully incorporated throughout the text. This Third Edition also features significantly revised coverage of lists, stacks, queues, and trees and an entire chapter dedicated to amortized analysis and advanced data structures such as the Fibonacci heap.
Known for its clear and friendly writing style, Data Structures and Algorithm Analysis in C++ is logically organized to cover advanced data structures topics from binary heaps to sorting to NP-completeness. Figures and examples illustrating successive stages of algorithms contribute to Weiss’ careful, rigorous and in-depth analysis of each type of algorithm.
Chapter 1 - Introduction
1.1 What’s the Book About?
1.2 Mathematics Review
1.3 A Brief Introduction to Recursion
1.4 C++ Classes
1.5 C++ Details
1.6 Templates
1.7 Using Matrices
Chapter 2 - Algorithm Analysis
2.1 Mathematical Background
2.2 Model
2.3 What to Analyze
2.4 Running Time Calculations
Chapter 3 - Lists, Stacks, and Queues
3.1 Abstract Data Types (ADTs)
3.2 The List ADT
3.3 vector and list in the STL
3.4 Implementation of vector
3.5 Implementation of list
3.6 The Stack ADT
3.7 The Queue ADT
Chapter 4 - Trees
4.1 Preliminaries
4.2 Binary Trees
4.3 The Search Tree ADT–Binary Search Trees
4.4 AVL Trees
4.5 Splay Trees
4.6 Tree Traversals (Revisited)
4.7 B-Trees
4.8 Sets and Maps in the Standard Library
Chapter 5 - Hashing
5.1 General Idea
5.2 Hash Function
5.3 Separate Chaining
5.4 Hash Tables Without Linked Lists
5.5 Rehashing
5.6 Hash Tables in the Standard Library
5.7 Extendible Hashing
Chapter 6 - Priority Queues (Heaps)
6.1 Model
6.2 Simple Implementations
6.3 Binary Heap
6.4 Applications of Priority Queues
6.5 d-Heaps
6.6 Leftist Heaps
6.7 Skew Heaps
6.8 Binomial Queues
6.9 Priority Queues in the Standard Library
Chapter 7 - Sorting
7.1 Preliminaries
7.2 Insertion Sort
7.3 A Lower Bound for Simple Sorting Algorithms
7.4 Shellsort
7.5 Heapsort
7.6 Mergesort
7.7 Quicksort
7.8 Indirect Sorting
7.9 A General Lower Bound for Sorting
7.10 Bucket Sort
7.11 External Sorting
Chapter 8 - The Disjoint Set Class
8.1 Equivalence Relations
8.2 The Dynamic Equivalence Problem
8.3 Basic Data Structure
8.4 Smart Union Algorithms
8.5 Path Compression
8.6 Worst Case for Union-by-Rank and Path Compression
8.7 An Application
Chapter 9 - Graph Algorithms
9.1 Definitions
9.2 Topological Sort
9.3 Shortest-Path Algorithms
9.4 Network Flow Problems
9.5 Minimum Spanning Tree
9.6 Applications of Depth-First Search
9.7 Introduction to NP-Completeness
Chapter 10 - Algorithm Design Techniques
10.1 Greedy Algorithms
10.2 Divide and Conquer
10.3 Dynamic Programming
10.4 Randomized Algorithms
10.5 Backtracking Algorithms
Chapter 11 - Amortized Analysis
11.1 An Unrelated Puzzle
11.2 Binomial Queues
11.3 Skew Heaps
11.4 Fibonacci Heaps
11.5 Splay Trees
Chapter 12 - Advanced Data Structures and Implementation
12.1 Top-Down Splay Trees
12.2 Red-Black Trees
12.3 Deterministic Skip Lists
12.4 AA-Trees
12.5 Treaps
12.6 k-d Trees
12.7 Pairing Heaps
Appendix A - Separate Compilation Of Class Templates