A Guide to Design and Analysis of Algorithms

Contents
Preface
Chapter 1
Introduction to the Design of Algorithms
	1.1. Brute Force Approach
		1.2. Divide and Conquer
	1.3. Greedy Technique
	1.4. Dynamic Programming
	1.5. Branch and Bound
	1.6. Randomized Algorithms
	1.7. Backtracking Algorithm
Chapter 2
Divide and Conquer
	2.1. Recurrence Relation
	2.2. Binary Search
	2.3. Merge Sort
Chapter 3
Greedy Algorithms
	3.1. Job Sequencing Problem with Deadline
	3.2. Dijkstra Algorithm
Chapter 4
Dynamic Programming
	4.1. Stagecoach Problem
	4.2. Optimal Binary Search Tree (Optimal BST)
		4.3. Subset Sum Problem
	4.4. 0/1 Knapsack Problem
Chapter 5
Backtracking
	5.1. N – Queens Problem
Chapter 6
Branch and Bound
	6.1. Assignment Problem
		6.1.1. Branch and Bound Technique to Solve Assignment Problem
	6.2. O/1 Knapsack Problem
	6.3. Travelling Salesman Problem
Chapter 7
Introduction to the Analysis of Algorithms
	7.1. Asymptotic Analysis
		7.1.1. Big – Oh
		7.1.2. Big - Omega
		7.1.3. Theta
	7.2. Empirical Analysis of Computer Algorithms:  Why Statistics?
		7.2.1. Computer Experiments and Algorithmic Complexity
		7.2.2. Statistical (Complexity) Bound (Definition)
Chapter 8
Randomized Algorithms
	8.1. Randomized Quick Sort
	8.2. Randomized Binary Search
Chapter 9
Master Theorem
	9.1. Master Theorem for Decreasing Function
	9.2. Limitations of Master Theorem
Chapter 10
A Note on Empirical Complexity Analysis
	10.1. The Fundamental Theorem of Finite Difference
	10.2. Empirical Complexity of Merge Sort
	10.3. Empirical Complexity of Quick Sort
	10.4. Empirical Complexity of Bubble Sort
	10.5. Empirical Complexity of Selection Sort
References
About the Authors
Index
Blank Page
Blank Page