Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.

Learn the basics of advanced data structures and algorithms. We will cover topics like Big O notation, complexity analysis, and the importance of choosing the right data structure and algorithm for a given problem.

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming ...

We will cover a broad selection of topics including amortization, hashing, dimensionality reduction, bit scaling, network flow, linear programming, and approximation algorithms.

Gain hands-on experience as you explore foundational concepts, analyze algorithmic complexities, and implement efficient solutions to real-world problems. The journey begins with recursion, where you’ll delve into function control, call stacks, and tree recursion, supplemented by practical examples like factorial computation.

Algorithms for Memory Hierarchies: Advanced Lectures (1st Edition), Lecture Notes in Computer Science, Springer, 2003. Why Parallelism? Unicore Performance Has Hit a Wall! “Everything that can be invented has been invented.” Exhausted all ideas to exploit hidden parallelism? ― So what happens if we overclock by 20%? ― And underclock by 20%?

This is a graduate course covering advanced topics in algorithm design and data structures. We will see a very wide variety of techniques and tools such as amortized analysis, randomization, parameterization, kernelization, and approximation algorithms.

Topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, ...

Overview: In this course, we will survey the field of algorithm design, with an eye towards theory. Our goal will be to design and analyze algorithms for solving problems of different sorts, whose solutions are non-obvious.

Introduction to Advanced Algorithms. In this lesson, we will explore the fascinating world of advanced algorithms and their importance in problem-solving. As a seasoned engineer with a strong background in coding, you understand the significance of algorithms in …