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Computer Algorithms 2

Computer Algorithms 2. Instructor: Prof. Dr. Shashank K. Mehta, Department of Computer Science and Engineering, IIT Kanpur. The course discusses efficient algorithms from a large number of domains. This course assumes the knowledge of data-structures, the knowledge big-O notation and the concept of time and space complexity of an algorithm. And also it will not introduce divide and conquer, dynamic programming, and greedy paradigms. This course will discuss graph algorithms, network-flow algorithms, geometric algorithms, string matching, matrix algorithms, polynomial computation algorithms, and number theoretic algorithms. (from nptel.ac.in)

Lecture 33 - Approximation Algorithms II


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Lecture 01 - Graph Basics
Lecture 02 - Breadth First Search
Lecture 03 - Dijkstra's Algorithm
Lecture 04 - All Pair Shortest Path
Lecture 05 - Matriods
Lecture 06 - Minimum Spanning Tree
Lecture 07 - Edmonds' Matching Algorithm I
Lecture 08 - Edmonds' Matching Algorithm II
Lecture 09 - Flow Networks
Lecture 10 - Ford-Fulkerson Method
Lecture 11 - Edmonds-Karp Algorithm
Lecture 12 - Matrix Inversion
Lecture 13 - Matrix Decomposition
Lecture 14 - Knuth-Morris-Pratt Algorithm
Lecture 15 - Rabin-Karp Algorithm
Lecture 16 - NFA Simulation
Lecture 17 - Integer-Polynomial Operations I
Lecture 18 - Integer-Polynomial Operations II
Lecture 19 - Integer-Polynomial Operations III
Lecture 20 - Chinese Remainder Theorem I
Lecture 21 - Chinese Remainder Theorem II
Lecture 22 - Chinese Remainder Theorem III
Lecture 23 - Discrete Fourier Transforms I
Lecture 24 - Discrete Fourier Transforms II
Lecture 25 - Discrete Fourier Transforms III
Lecture 26 - Schonhage-Strassen Algorithm
Lecture 27 - Linear Programming I
Lecture 28 - Linear Programming II
Lecture 29 - Geometric Algorithms I
Lecture 30 - Geometric Algorithms II
Lecture 31 - Geometric Algorithms III
Lecture 32 - Approximation Algorithms I
Lecture 33 - Approximation Algorithms II
Lecture 34 - Approximation Algorithms III
Lecture 35 - Dynamic Programming