Approximation Algorithms Part I

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( 34 Reviews )

Created by: Claire Mathieu

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Overall Score : 92 / 100

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Course Description

Approximation algorithms, Part IHow 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.

Time: 22h

Instructor Details

Claire Mathieu

4.6 Rating
34 Reviews

CoursesApproximation Algorithms Part IIApproximation Algorithms Part I

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4.6

34 total reviews

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By Jon L B on 17-Sep-17

This course is awesome. Prof. managed to elaborate the problem and analysis clearly and homework is properly assigned.

By Angel E G J on 3-Jan-17

I love how this course delves into a very promising advanced research topic in Computational Complexity. It helps me a lot to understand trending publications in the area. Specially that the material is presented in an incremental approach. This way a researcher can live through the evolution of ideas. It is inspiring also in the way one comes up with a partial solution for the special case, and then generalizes with approximation factor that is satisfyingly good. I'm looking forward to more advanced courses in parameterized, streaming or quantum algorithms.

By Jose M on 5-Feb-16

A useful course which introduces key ideas in Approximation Algorithms. Looking forward to part II.

By Pietro on 30-May-17

super cool!

By Leo B on 11-Jan-17

This class is very clear and easy to understand! Thank you for providing such feast for students!

By Milagros G on 16-Jan-16

awesome course!I'd like to see part 2 and other graduate-level algorithms courses on coursera.

By Luis A G D M on 23-Jan-16

The course deals not with programming, but rather with designing and analyzing approximation algorithms. The course is on a high level and focuses on the subject without too much side remarks on applications, connections to other subjects or motivational introductions. If you are a theory person, however, you will probably enjoyed it.

By Marlon A Z Z on 27-Jan-16

The course provides a high-level introduction to approximation algorithm. There is no programming assignments but it provides nice introduction to approximation algorithm.

By Juan J N M on 23-Jun-16

Excellent!

By Jorge G P on 13-Feb-17

Great course!

By Andres M on 21-Sep-17

Very good !

By Pedro P on 8-Feb-16

Really good course, the material is quite advanced but very well structured and introduced in a very simple way. The assignments were a lot of fun: really enjoyed the peer-graded assignments where I needed to write short proofs, much more useful than regular multiple-choice answers. Looking forward to the next course!

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