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Approximation Algorithms Part I

École normale supérieure via Coursera

Overview

Approximation algorithms, Part I

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

This course assumes knowledge of a standard undergraduate Algorithms course, and particularly emphasizes algorithms that can be designed using linear programming, a favorite and amazingly successful technique in this area. By taking this course, you will be exposed to a range of problems at the foundations of theoretical computer science, and to powerful design and analysis techniques. Upon completion, you will be able to recognize, when faced with a new combinatorial optimization problem, whether it is close to one of a few known basic problems, and will be able to design linear programming relaxations and use randomized rounding to attempt to solve your own problem. The course content and in particular the homework is of a theoretical nature without any programming assignments.

This is the first of a two-part course on Approximation Algorithms.

Taught by

Claire Mathieu

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Reviews

5.0 rating, based on 2 reviews

Start your review of Approximation Algorithms Part I

  • Anonymous

    Anonymous completed this course.

    I thought this to be an excellent course on some tricky topics. Expect to be both entertained and challenged at the same time. After competing the course there is a complete buzz. Don't forget about part 2.. totally awesome too
  • Ali Shakiba completed this course.

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