Subject code AD3351 deals with semester III of B.Tech Artificial Intelligence and Data Science regarding affiliated institutions of Anna University Regulation 2021 Syllabus. In this article, you can gather certain information relevant to the Design And Analysis Of Algorithms. We added the information by expertise.
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If you want to know more about the syllabus of B.Tech Artificial Intelligence And Data Science connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.Tech. Artificial Intelligence And Data Science Syllabus Anna University, Regulation 2021.
Aim of Objectives:
- To critically analyze the efficiency of alternative algorithmic solutions for the same problem.
- To illustrate brute force and divide and conquer design techniques.
- To explain dynamic programming and greedy techniques for solving various problems.
- To apply iterative improvement technique to solve optimization problems.
- To examine the limitations of algorithmic power and handling it in different problems.
AD3351 – Design And Analysis Of Algorithms Syllabus
Unit I: Introduction
Notion of an Algorithm – Fundamentals of Algorithmic Problem Solving – Important Problem Types –Fundamentals of the Analysis of Algorithm Efficiency – Analysis Framework – Asymptotic Notations and their properties – Empirical analysis – Mathematical analysis of Recursive and Non recursive algorithms – Visualization.
Unit II: Brute Force And Divide And Conquer
Brute Force – String Matching – Exhaustive Search – Traveling Salesman Problem – Knapsack Problem – Assignment problem. Divide and Conquer Methodology – Multiplication of Large Integers and Strassen’s Matrix Multiplication – Closest-Pair and Convex – Hull Problems. Decrease and Conquer: – Topological Sorting – Transform and Conquer: Presorting – Heaps and Heap Sort.
Unit III: Dynamic Programming And Greedy Technique
Dynamic programming – Principle of optimality – Coin changing problem – Warshall’s and Floyd‘s algorithms – Optimal Binary Search Trees – Multi stage graph – Knapsack Problem and Memory functions. Greedy Technique – Dijkstra’s algorithm – Huffman Trees and codes – 0/1 Knapsack problem.
Unit IV: Iterative Improvement
The Simplex Method – The Maximum-Flow Problem – Maximum Matching in Bipartite Graphs – The Stable marriage Problem.
Unit V: Limitations Of Algorithm Power
LowerBound 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 – Traveling Salesman Problem – Approximation Algorithms for NP-Hard Problems – Traveling Salesman problem – Knapsack problem.
Text Books:
Anany Levitin, Introduction to the Design and Analysis of Algorithms, Third Edition, Pearson Education, 2012.
References:
- Ellis Horowitz, Sartaj Sahni and Sanguthevar Rajasekaran, Computer Algorithms/ C++, Second Edition, Universities Press, 2019.
- Thomas H.Cormen, Charles E.Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, PHI Learning Private Limited, 2012.
- S. Sridhar, Design and Analysis of Algorithms, Oxford University Press, 2014.
- Alfred V. Aho, John E. Hopcroft and Jeffrey D. Ullman, Data Structures and Algorithms, Pearson Education, Reprint 2006.
Related Posts On Semester – III:
- MA3354 Discrete Mathematics
- CS3351 Digital Principles and Computer Organization
- AD3391 Database Design and Management
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