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 
Lecture Notes on Design and Analysis of Algorithms
These ecture notes give a comprehensive introduction to the basic techniques in the design and analysis of algorithms. It covers major methodologies, including greedy methods, which build up solutions piece by piece; dynamic programming (DP), which breaks down problems into simpler subproblems, solves them, and memorizes their solutions; and backtracking, which incrementally generates candidates for solutions and discards those that cannot satisfy criteria. It also discusses the method of Branch and Bound, where all branches on a solution space are systematically explored until the best possible solution is obtained. These methods are crucial in the design of nice algorithms with a view to efficiency, and they form the basis of complex computational problems that require solutions.
Author(s): Annamalai University
96 Pages
Advanced Algorithms by Anupam Gupta
These all are very extensive notes on fairly advanced topics in algorithms—both theoretical and practical. Here we deal with discrete algorithms for minimum spanning trees, arborescences (directed spanning trees), dynamic algorithms for problems in graph connectivity, and the shortest path. Other topics discussed in the paper are the combinatorial, algebraic algorithms for graph matching techniques and their corresponding challenges developed within high-dimensional spaces via the technique of dimension reduction and streaming algorithms. Other topics but not triangulated within include the approximate max-flows, online learning, and interior-point methods. The notes thus present a framework in its totality for learning and analyzing super advanced algorithms and thus become a good source to glean insights for an ocean of problems in computer science.
Author(s): Anupam Gupta
309 Pages
Lecture Notes On Design And Analysis Of Algorithms
Dr. Subasish Mohapatra's Lecture Notes on Design and Analysis of Algorithms, published on August 2, 2022, is a big 125-pages book to cover many of the algorithmic core ideas. It contains the core ideas such as growth of functions and recurrences, with great detail on the solving of these via substitution, recursion trees, and the Master Method. Discussion of detailed Divide and Conquer algorithms, along with worst-case running times for problems such as merge sort, quick sort, and binary search, is also covered. Heaps, heap sort, priority queues, and sorting lower bounds are discussed. Detailed discussions of dynamic programming techniques cover the matrix chain multiplication problem, longest common subsequence problem, and general strategies. Discussions of robust algorithms based on dynamic programming that cover applications such as the activity selection problem, fractional knapsack problem, and Huffman coding. It also comprises graph algorithms, for instance: BFS, DFS, minimum spanning trees, Kruskal's, and Prim's.
Author(s): Dr. Subasish Mohapatra
125 Pages
Fundamentals of Algorithms with Applications
Michael T. Goodrich's Fundamentals of Algorithms with Applications gives good coverage to algorithmic principles and their application. It covers growth functions, basic data structures, sorting, selection, dynamic programming, graph algorithms-the principles of algorithm design. Advanced topics such as NP-completeness, approximation algorithms, and randomized algorithms are also explored. Goodrich's book is well-recognized for its lucid explanations of the exercises on these complex topics to make them understandable and lively. Theoretically sound, with practical applications, this book suits both students and professionals in developing problem-solving skills and computational understanding.
Author(s): Michael T. Goodrich
NA Pages
Advanced Algorithms by Prof. Michel Goemans
Advanced Algorithms" by Prof. Michel Goemans is an advanced-level text focused on sophisticated algorithmic methods for doctoral students and researchers. Advanced subjects like Fibonacci heaps, network flows, and dynamic trees are explained in detail, together with linear programming-the Goldberg-Tarjan min-cost circulation algorithm, approximation algorithms, max-cut problems, and conic programming. Goemans explains such advanced concepts in great detail, merging theory and practice. This text will be useful for anyone interested in deeply understanding modern algorithms and how they may be implemented and includes a conceptual framework for rigorous solutions to complex computational problems.
Author(s): Prof. Michel Goemans
NA Pages
Data Structures And Algorithms by Sugih Jamin
The book Data Structures and Algorithms by Sugih Jamin covers all the basic concepts of Computer Science in a very balanced way. It involves topics such as linked lists, stacks, and queues to more advanced topics such as binary search trees, heaps, and balanced search trees. Jama's text emphasizes an implementation perspective and algorithmic patterns, which will facilitate a more effective way of understanding and applying the concepts presented. This book will be very useful for the students and professionals who want to establish a sound foundation in data structures and algorithms by providing a solid theoretical background supported by practical examples that explain how problems are solved.
Author(s): Sugih Jamin
NA Pages
