Introduction:What is an Algorithm?, Fundamentals of Algorithmic Problem Solving.

Fundamentals of the Analysis of Algorithm Efficiency: Analysis Framework, Asymptotic Notations and Basic Efficiency Classes, Mathematical Analysis of Non recursive Algorithms, Mathematical Analysis of Recursive Algorithms.

Brute Force Approaches: Selection Sort and Bubble Sort, Sequential Search and Brute Force String Matching.

Brute Force Approaches (contd..): Exhaustive Search (Travelling Salesman problem and Knapsack Problem).

Decrease and Conquer: Insertion Sort, Topological Sorting.

Divide and Conquer: Merge Sort, Quick Sort, Binary Tree Traversals, Multiplication of Large Integers and Strassen’s Matrix Multiplication.

Transform and-Conquer: Balanced Search Trees, Heaps and Heapsort.

Space Time Tradeoffs: Sorting by Counting: Comparison counting sort, Input Enhancement in String Matching: Horspool’s Algorithm.

Dynamic Programming: Three basic examples, The Knapsack Problem and Memory Functions, Warshall’s and Floyd’s Algorithms.

The Greedy Method: Prim’s Algorithm, Kruskal’s Algorithm, Dijkstra’s Algorithm, Huffman Trees and Codes.

Limitations of Algorithmic Power: Decision Trees, P, NP, and NP-Complete Problems.

Copying with Limitations of Algorithmic Power: Backtracking (n-Queens problem, Subset-sum problem), Branch-and-Bound (Knapsack problem), Approximation algorithms for NP-Hard problems (Knapsack problem).