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Design and Analysis of Algorithms

Design and Analysis of Algorithms. Instructor: Prof. Abhiram Ranade, Department of Computer Science, IIT Bombay. This course covers lessons on divide and conquer, greedy algorithm, pattern matching, dynamic programming and approximation algorithm. The main goal of this course teaches you to design algorithms which are fast. In this course you will study well defined design techniques through lots of exercises. We hope that at the end of the course you will be able to solve algorithm design problems that you may encounter later in your life. (from nptel.ac.in)

Lecture 17 - Combinational Search and Optimization II

Go to the Course Home or watch other lectures:

Lecture 01 - Overview

Lecture 02 - Framework for Algorithms Analysis

Lecture 03 - Framework for Algorithms Analysis (cont.)

Lecture 04 - Asymptotic Notations

Lecture 05 - Algorithm Design Techniques: Basics

Lecture 06 - Divide-and-Conquer

Lecture 07 - Divide-and-Conquer: Median Finding

Lecture 08 - Divide-and-Conquer: Surfing Lower Bounds

Lecture 09 - Divide-and-Conquer: Closest Pair

Lecture 10 - Greedy Algorithms I

Lecture 11 - Greedy Algorithms II

Lecture 12 - Greedy Algorithms III

Lecture 13 - Greedy Algorithms IV

Lecture 14 - Pattern Matching I

Lecture 15 - Pattern Matching II

Lecture 16 - Combinational Search and Optimization I

Lecture 17 - Combinational Search and Optimization II

Lecture 18 - Dynamic Programming

Lecture 19 - Longest Common Subsequences

Lecture 20 - Matrix Chain Multiplication

Lecture 21 - Scheduling with Startup and Holding Costs

Lecture 22 - Average Case Analysis of Quicksort

Lecture 23 - Bipartite Maximum Matching

Lecture 24 - Lower Bounds for Sorting

Lecture 25 - Element Distinctness Lower Bounds

Lecture 26 - NP-Completeness I

Lecture 27 - NP-Completeness II

Lecture 28 - NP-Completeness III

Lecture 29 - NP-Completeness IV

Lecture 30 - NP-Completeness V

Lecture 31 - NP-Completeness VI

Lecture 32 - Approximation Algorithms for NP-Complete Problems

Lecture 33 - Approximation Algorithms for NP-Complete Problems (cont.)

Lecture 34 - Approximation Algorithms for NP-Complete Problems (cont.)