Advanced Algorithms

parallel and distributed algorithms

Zero Lecture

The course of Parallel and Distributed Algorithms has prerequites: Data Structures, Algorithms, Discrete Mathematical structures, and Theory of Computation

Course Objectives

The course is aimed to impart the in depth advanced and design and Analysis knowledge about modern parallel and Distributed Algorithms running on multiprocessor architecture and in a distributed architecture.

Course Outcomes

The course will result in acquiring the advanced knowledge on the various topics in parallel and distributed algorithms.

Module 1: parallel algorithms


matching problem
Network flows and maximum flow problems
geometrical algorithms: applications, divide and conquer, convexity
geometrical algorithsm: convex hull, proximity, voronoi diagrams
parallel algorithms (concepts, prams, interconnection networks) 
parallel algorithms (work-depth models, design of parallel algorithms)
Randomized algorithms  
random variable
home assignment#2
approximation algorithm
self adjusting, persistent and multidimensional data structres
Introduction to Natural Language Processing-I

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