Distributed Algorithms Lecture Notes
Prof. Nancy Lynch's Distributed Algorithms Lecture Notes has a great amount of detail concerning algorithms designed for distributed systems within which important aspects are that of multiple processors executing without centralized control. This paper investigates the model assumptions and organization strategies tasked with the two basic timing models. It also looks at synchronous, asynchronous, and partially synchronous models and synchronous networks. They discuss various models, thus enable the researchers to understand what one is actually up against and what strategies one can use in order to design algorithms working effectively in distributed environments. Hence, Lynch's notes are a must-have for any researcher who aims to know how to manage communication and coordination in distributed systems. Therefore, ideal for use by students and professionals dealing with distributed computing and networked systems.
Author(s): Prof. Nancy Lynch
NA Pages 
Lecture Notes on Design and Analysis of Algorithms
This note describes the following topics, Greedy methods, Dynamic programming, Backtracking, Branch and bound.
Author(s): Annamalai University
96 Pages
Advanced Algorithms by BMS College of Engineering
he PDF covers the following topics related to Computer Algorithms, Dynamic programming, Application domain of DP, Matrix Chain Multiplication, MCP DP Steps, Recursive Tree, Longest Increasing Subsequence, Dynamic programming, Rod cutting 4 inch rod example, DP for rod cutting, Bottom up approach.
Author(s): BMS College of Engineering
152 Pages
Lecture Notes On Design And Analysis Of Algorithms
This note covers the following topics, Design and analysis of algorithms, Growth of Functions, Recurrences, Solution of Recurrences by substitution, Recursion tree method, Master Method, Worst case analysis of merge sort, quick sort and binary search, Design and analysis of Divide and Conquer Algorithms, Heaps and Heap sort, Priority Queue, Lower Bounds for Sorting, Dynamic Programming algorithms, Matrix Chain Multiplication, Elements of Dynamic Programming, Longest Common Subsequence, Greedy Algorithms, Activity Selection Problem, Elements of Greedy Strategy, Fractional Knapsack Problem, Huffman Codes, Graph Algorithm BFS and DFS, Minimum Spanning Trees, Kruskal algorithm, Prims Algorithm, Fourier transforms and RabinKarp Algorithm.
Author(s): Dr. Subasish Mohapatra
125 Pages
Design And Analysis Of Algorithms by Herbert Edelsbrunner
This book focuses on fundamental data structures and graph algorithms. The emphasis will be on algorithm design and on algorithm analysis.
Author(s): Herbert Edelsbrunner
95 Pages
Advanced Algorithms Lectures by Shuchi Chawla
Advanced Algorithms Lectures by Shuchi Chawla give an insight into advanced techniques in the design and analysis of algorithms. The lectures cover topics such as greedy algorithms, dynamic programming, and network flow applications. Advanced topics, including randomized algorithms and Kargers mincut algorithm, NPcompleteness, together with linear programming, primaldual algorithms, and semidefinite programming, are discussed. Chawla also deals with models like Probably Approximately Correct PAC and boosting within this framework. This set of lectures comprehensively covers advanced algorithmic methodologies along with their applications and constitutes an excellent resource for students and researchers interested in advanced classes of algorithmic techniques and their applications to pressing realworld problems.
Author(s): Shuchi Chawla
195 Pages
The lecture notes on Approximation Algorithms by Shuchi Chawla focus on techniques of designing algorithms that produce nearoptimal solutions to complex optimization problems for which finding an exact solution is computationally infeasible. These lecture notes cover general underlying techniques of approximation algorithms, comprising basic building blocks and the foundation needed to deal with problems which are difficult to solve exactly due to computational complexity. These notes by Chawla provide an outline of various methods for approaching different optimization problems and ways of solving them when exact algorithms are not practical. Further, this resource is likely to be extremely helpful with respect to devising and applying approximation algorithms returning good solutions within a reasonable amount of time, hence, this is a must for scholars and practitioners faced with hard optimization problems.
Author(s): Shuchi Chawla
NA Pages
