Approximation Algorithms

Many real-world algorithmic issues, for example, are NP-hard and cannot be addressed effectively using typical algorithmic methods. The purpose of the Approximation Algorithms is to familiarize students with key algorithmic principles and approaches that are required to properly solve such issues. These strategies are useful when they don't need the exact answer to a problem, but rather an estimate that is near to it. They will look at how to find such approximations quickly. Prerequisites: You need have a basic understanding of algorithms and mathematics in order to succeed in this course.

Here's a quick rundown of what you should know: O-notation, -notation, -notation; algorithm analysis; Fundamental calculus: handling summations, solving recurrences, and working with logarithms, among other things; Probability theory fundamentals: occurrences, probability distributions, random variables, expected values, and so on; Linked lists, stacks, queues, and heaps are examples of basic data structures; Binary search trees (balanced); Basic graph concepts, representations of graphs (adjacency lists and adjacency matrices), and basic graph algorithms (BFS, DFS, topological sort, shortest routes).

The Approximation Algorithms materials are based on the course notes, which may be accessed under the resources page. They will not go over all of the material in the course notes. The course notes are available for students who did not fully comprehend the lectures as well as those who want to learn more about the topics. There are a few small errors in the video lectures. A list of these blunders may be found in the resources section (in the document called "Errata"). If you believe you have discovered a mistake, please report it by clicking the square flag at the bottom of the lecture or quiz where the error was discovered.

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• Intermediate level
• Approx. 3 p.m. to complete
• Subtitles: English

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Enroll here: coursera.org/learn/approximation-algorithms