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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.
| ISBN | 0321397339 | | Pages | 608 | | ISBN13 | 9780321397331
(What's this?) | | Weight (grammes) | 908 | | Publisher | Pearson Education (US) | | Published in | Upper Saddle River | | Imprint | Pearson Education (US) | | Previous ISBN | 9780321441461 | | Format | Paperback | | Height (mm) | 235 | | Publication date | 01 Nov 2005 | | Width (mm) | 191 | | DEWEY | 005.133 | | Academic level | Professional / Scholarly | | DEWEY edition | DC22 | |
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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
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