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Design and Analysis of Algorithms for Anna University R17 (IV CSE/IT - CS8451)
UNIT - I Introduction
Notion of an Algorithm - Fundamentals of Algorithmic Problem Solving - Important Problem Types - Fundamentals of the Analysis of Algorithmic Efficiency - Asymptotic Notations and their properties. Analysis Framework - Empirical analysis - Mathematical analysis for Recursive and Non-recursive algorithms - Visualization. (Chapter - 1)
UNIT - II Brute Force and Divide-and-Conquer
Brute Force - Computing - String Matching - Closest-Pair and Convex-Hull Problems - Exhaustive Search - Travelling Salesman Problem - Knapsack Problem - Assignment problem. Divide and Conquer Methodology - Binary Search - Merge sort - Quick sort - Heap Sort - Multiplication of Large Integers - Closest-Pair and Convex - Hull Problems. (Chapter - 2)
UNIT - III Dynamic Programming and Greedy Technique
Dynamic programming - Principle of optimality - Coin changing problem, Computing a Binomial Coefficient - Floyd's algorithm - Multi stage graph - Optimal Binary Search Trees - Knapsack Problem and Memory functions. Greedy Technique - Container loading problem - Prim's algorithm and Kruskal's Algorithm - 0/1 Knapsack problem, Optimal Merge pattern - Huffman Trees. (Chapter - 3)
UNIT - IV Iterative Improvement
The Simplex Method - The Maximum-Flow Problem - Maximum Matching in Bipartite Graphs, Stable marriage Problem. (Chapter - 4)
UNIT - V Coping with the Limitations of Algorithm Power
Lower - Bound Arguments - P, NP NP- Complete and NP Hard Problems. Backtracking - n-Queen problem - Hamiltonian Circuit Problem - Subset Sum Problem. Branch and Bound - LIFO Search and FIFO search - Assignment problem - Knapsack Problem - Travelling Salesman Problem - Approximation Algorithms for NP-Hard Problems - Travelling Salesman problem - Knapsack problem. (Chapter - 5)
UNIT - I Introduction
Notion of an Algorithm - Fundamentals of Algorithmic Problem Solving - Important Problem Types - Fundamentals of the Analysis of Algorithmic Efficiency - Asymptotic Notations and their properties. Analysis Framework - Empirical analysis - Mathematical analysis for Recursive and Non-recursive algorithms - Visualization. (Chapter - 1)
UNIT - II Brute Force and Divide-and-Conquer
Brute Force - Computing - String Matching - Closest-Pair and Convex-Hull Problems - Exhaustive Search - Travelling Salesman Problem - Knapsack Problem - Assignment problem. Divide and Conquer Methodology - Binary Search - Merge sort - Quick sort - Heap Sort - Multiplication of Large Integers - Closest-Pair and Convex - Hull Problems. (Chapter - 2)
UNIT - III Dynamic Programming and Greedy Technique
Dynamic programming - Principle of optimality - Coin changing problem, Computing a Binomial Coefficient - Floyd's algorithm - Multi stage graph - Optimal Binary Search Trees - Knapsack Problem and Memory functions. Greedy Technique - Container loading problem - Prim's algorithm and Kruskal's Algorithm - 0/1 Knapsack problem, Optimal Merge pattern - Huffman Trees. (Chapter - 3)
UNIT - IV Iterative Improvement
The Simplex Method - The Maximum-Flow Problem - Maximum Matching in Bipartite Graphs, Stable marriage Problem. (Chapter - 4)
UNIT - V Coping with the Limitations of Algorithm Power
Lower - Bound Arguments - P, NP NP- Complete and NP Hard Problems. Backtracking - n-Queen problem - Hamiltonian Circuit Problem - Subset Sum Problem. Branch and Bound - LIFO Search and FIFO search - Assignment problem - Knapsack Problem - Travelling Salesman Problem - Approximation Algorithms for NP-Hard Problems - Travelling Salesman problem - Knapsack problem. (Chapter - 5)
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