Data Structures And Algorithms by Sugih Jamin
This note covers the following topics: Algorithm Analysis, algorithmic patterns, Standard I/O and iostream, Foundational Data Structures and Basic Abstract Data Types, Linked-list, Stacks and Queues, PA1 walkthrough, Pointer, Hashing, Recursion and Recurrence Relations, Trees, Binary Search Trees, Range and Multidimensional Searches, Heaps, Tries, Balanced Search Trees, Binary-tree Representations of Multi-way Trees.
Author(s): Sugih Jamin
NA Pages 
Lecture Notes On Design And Analysis Of Algorithms
This note explains the following topics related to Algorithm Analysis and Design: Introduction to Design and analysis of algorithms, Growth of Functions, Recurrences, Solution of Recurrences by substitution,Recursion tree method, Master Method, Design and analysis of Divide and Conquer Algorithms, Worst case analysis of merge sort, quick sort and binary search, Heaps and Heap sort, Priority Queue, Lower Bounds for Sorting.
Author(s): Mr. S.K. Sathua, Dr. M.R. Kabat and Dr. R. Mohanty
80 Pages
Fundamentals of Algorithms with Applications
This note explains the following topics: Growth of functions, Basic data structures, Sorting and Selection, Fundamental techniques, Dynamic programming and Graphs, Graph algorithms, NP-Completeness and approximation algorithms, Randomized Algorithms.
Author(s): Michael T. Goodrich
NA Pages
Advanced Algorithms by Prof. Michel Goemans
This note is designed for doctoral students interested in theoretical computer science. Topics covered includes: Fibonacci heaps, Network flows, Maximum flow, minimum cost circulation, Goldberg-Tarjan min-cost circulation algorithm, Cancel-and-tighten algorithm; binary search trees, Splay trees, Dynamic trees , Linear programming, LP: duality, geometry, simplex, LP: complexity, ellipsoid algorithm, LP: applications of the ellipsoid algorithm, Conic programming, Approximation algorithms, Max-cut and sparsest-cut, Multi-commodity flows and metric embeddings, Convex hulls.
Author(s): Prof. Michel Goemans
NA Pages
Distributed Algorithms Lecture Notes
Distributed algorithms are algorithms designed to run on multiple processors, without tight centralized control. Topics covered includes: Variations in model assumptions, Top-level organization is by the timing model, Synchronous model, Asynchronous model, Partially synchronous model, Synchronous networks.
Author(s): Prof. Nancy Lynch
NA Pages
The field of approximation algorithms has developed in response to the difficulty in solving a good many optimization problems exactly. This note will present general techniques that underly these algorithms.
Author(s): Shuchi Chawla
NA Pages
Introduction to Algorithms Lecture Notes
This note concentrates on the design of algorithms and the rigorous analysis of their efficiency. Topics covered includes: the basic definitions of algorithmic complexity, basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications, graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis.
Author(s): Michael Dinitz
NA Pages
