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, Prim's Algorithm, Fourier transforms and Rabin-Karp Algorithm.

**Author(s):** Dr. Subasish
Mohapatra

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

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

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

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

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

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

This note introduces students to advanced techniques for the design and analysis of algorithms, and explores a variety of applications. Topics covered includes: Greedy algorithms, Dynamic programming, Network flow applications, matchings, Randomized algorithms, Karger's min-cut algorithm, NP-completeness, Linear programming, LP duality, Primal-dual algorithms, Semi-definite Programming, MB model contd., PAC model, Boosting in the PAC framework.

**Author(s):** Shuchi Chawla

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

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

This note will cover classic and modern algorithmic ideas that are central to many areas of Computer Science. The focus is on most powerful paradigms and techniques of how to design algorithms, and measure their efficiency. The topics will include hashing, sketching, dimension reduction, linear programming, spectral graph theory, gradient descent, multiplicative weights, compressed sensing, and others.

**Author(s):** Alex Andon

This book explains the following topics: Stable Matchings, Algrithm Design by Induction, Graphs, Trees or BFS, Connected Comps/Bipartite Graphs, DFS or Topological Ordering, Interval Scheduling, Interval Partitioning, MST, MST, Union find, Closest Points, Master Theorem, Integer Multiplication, Median, Vertex Cover or Set Cover, Network Connectivity, Image Segmentation, Reductions, NP-Completeness, Linear Programming.

**Author(s):** Shayan Oveis Gharan

This is an intermediate algorithms course note with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

**Author(s):** Prof. Erik Demaine, Prof. Srinivas
Devadas and Prof. Nancy Lynch

This note explains core material in data structures and algorithm design, and also helps students prepare for research in the field of algorithms. Topics covered includes: Splay Trees, Amortized Time for Splay Trees, Maintaining Disjoint Sets, Binomial heaps, F-heap, Minimum Spanning Trees, Fredman-Tarjan MST Algorithm, Light Approximate Shortest Path Trees, Matchings, Hopcroft-Karp Matching Algorithm, Two Processor Scheduling, Network Flow - Maximum Flow Problem, The Max Flow Problem and Max-Flow Algorithm.

**Author(s):** Samir Khuller

This note covers the following topics: Self adjusting data structures, amortized analysis, self adjusting lists, Splay trees, their performance and related conjectures, Hashing, FKS perfect hashing, Cuckoo hasing, dynamic perfect hashing, Fusion Trees, Fully dynamic connectivity in polylogarithmic time, Dynamic all pairs shortest paths, Linear time construction of Suffix trees and arrays, Succinct Data Structures, External memory data structures, Geometric data structures, Top trees, Retroactive data structures, Online optimal structure for planar point location.

**Author(s):** Venkatesh Raman

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

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

This note will examine various data structures for storing and accessing information together with relationships between the items being stored, and algorithms for efficiently finding solutions to various problems, both relative to the data structures and queries and operations based on the relationships between the items stored. Topics covered includes: Algorithm analysis, List, stacks and queues, Trees and hierarchical orders, Ordered trees, Search trees, Priority queues, Sorting algorithms, Hash functions and hash tables, Equivalence relations and disjoint sets, Graph algorithms, Algorithm design and Theory of computation.

**Author(s):** Douglas Wilhelm Harder

This note covers the following topics: Divide And Conquer Strategy, Greedy Method, Minimum Spanning Trees, Dynamic Programming, Optimal Binary search trees, Backtracking Method, Branch and Bound, Lower bound Theory.

**Author(s):** Sh. Ganesh
Kumar

This note introduces a number of important algorithm design techniques as well as basic algorithms that are interesting both from a theoretical and also practical point of view. Topics covered are: Introduction to Algorithms, Asymptotic Analysis, Recurrence Equations, Sorting Algorithms, Search Trees, Randomized Algorithms and Quicksort, Selection Algorithms, Number Theory and Cryptography Algorithms, Graph algorithms, Greedy Algorithms and External Memory Algorithms.

**Author(s):** Department of Computer
Science at Duke University

This note covers the following topics: Introduction to Algorithms, Asymptotic Notation, Modeling or Logarithms, Elementary Data Structures, Dictionary data structures, Sorting, Heapsort or Priority Queues, Recurrence Relations, Introduction to NP-completeness, Reductions, Cook's Theorem or Harder Reduction, NP-completeness challenge, Approximation Algorithms and Heuristic Methods.

**Author(s):** Steven Skiena

This book is designed as a teaching text that covers most standard data structures, but not all. A few data structures that are not widely adopted are included to illustrate important principles.

**Author(s):** Clifford A. Shaffer

This book is a concise introduction to this basic toolbox, intended for students and professionals familiar with programming and basic mathematical language.

**Author(s):** K. Mehlhorn and P. Sanders

This note covers the following topics: Fundamentals of data structure, simple data structures, ideas for algorithm design, the TABLE Data Type, free storage management, sorting, storage on external media, variants on the SET Data Type, pseudo-random numbers, data compression, algorithms on graphs, algorithms on strings and Geometric Algorithms.

**Author(s):** Martin Richards

This lecture note explains data structures and algorithms, focusing on advanced topics such as graph theory, randomized algorithms, and combinatorial search.

**Author(s):** Ray
Toal

This note covers the following topics: Algorithms and Data Structures, Introduction to Java, Software Development, Writing Classes, Writing Classes in Java, Unit Testing, Building Large Java Applications, Inheritance and Polymorphism, Interfaces, A Math Review, Algorithm Analysis, Data Types versus Data Structures, Collections, Stacks ,Queues, Lists, Recursion, Sorting, Trees, Oriented Trees, Ordered Trees, Binary Trees, Sets and Dictionaries, Search Trees, Binary Search Trees, Red-Black Trees.

**Author(s):** Ray Toal

This note covers the following topics: Introduction, Stable Matching, Graph Algorithms, Greedy Algorithms, Minimum Spanning Trees, Recurrences, Dynamic programming, Network Flow and Network Flow Applications.

**Author(s):** Richard Anderson

This lecture note discusses the approaches to designing optimization algorithms, including dynamic programming and greedy algorithms, graph algorithms, minimum spanning trees, shortest paths, and network flows. Also it briefly discusses algorithmic problems arising from geometric settings, that is, computational geometry.

**Author(s):** David M. Mount

This course note provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems.

**Author(s):** Prof.
Erik Demaine, Prof. Ronald Rivest and Prof. Srinivas Devadas

This note covers the following topics related to Algorithm Analysis and Design: Model and Analysis, Warm up problems, Brute force and Greedy strategy, Dynamic Programming, Searching, Multidimensional Searching and Geometric algorithms, Fast Fourier Transform and Applictions, String matching and finger printing, Graph Algorithms, NP Completeness and Approximation Algorithms.

**Author(s):** Sandeep Sen

This note covers the following topics: Mathematics for Algorithmic, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Amortized Analysis, Hash Table, Binary Search Tree, Graph Algorithms, String Matching, Sorting and Approximate Algorithms

**Author(s):** Rashid
Bin Muhammad

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**Author(s):**

This note covers the following topics: Encryption Algorithms, Genetic Algorithms, Geographic Information Systems Algorithms, Sorting Algorithms, Search Algorithms, Tree Algorithms, Computational Geometry Algorithms, Phonetic Algorithms and Project Management Algorithms.

**Author(s):** algosort.com

This note covers the design of algorithms according to methodology and application. Methodologies include: divide and conquer, dynamic programming, and greedy strategies. Applications involve: sorting, ordering and searching, graph algorithms, geometric algorithms, mathematical (number theory, algebra and linear algebra) algorithms, and string matching algorithms.

**Author(s):** Shai
Simonson

The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particular chapter 6, make simple use of a little advanced mathematics.

**Author(s):** C.J.
van Rijsgergen

This book explains the following topics: intrinsic complexity of computational tasks, Computational Complexity, P, NP, and NP-Completeness, relations between various computational phenomena.

**Author(s):** Oded
Goldreich

This note covers the following topics: Computational Models, Complexity measures, Power increasing resourses, Basic relatton amomg the models and measures, Reducibility, completeness and closure under reductions, Deterministics and nondeterministics logarithmic space, Deterministics polynomial time, Polynomial Hierarchy and Polynomial space.

**Author(s):** Martin
Tompa

This thesis presents efficient algorithms for internal and external parallel sorting and remote data update. Topics covered includes: Internal Parallel Sorting, External Parallel Sorting, The rsync algorithm, rsync enhancements and optimizations and Further applications for rsync.

**Author(s):** Andrew
Tridgell

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