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.
awesome course!I'd like to see part 2 and other graduate-level algorithms courses on coursera.
This was a relatively easy but well paced introduction to approximation algorithms. I totally enjoyed it.
Excellent Course! I have learnt a lot about Approximation Algorithms in a short span of time.
Excellent Course Really helped me to have an in depth knowledge in every concept
Great class, and Professor Claire Mathieu is doing an excellent job!
A great course if you want to learn about approximation algorithms from the point of view of linear programming relaxation!
This course is awesome. Prof. managed to elaborate the problem and analysis clearly and homework is properly assigned.
The course provides a high-level introduction to approximation algorithm. There is no programming assignments but it provides nice introduction to approximation algorithm.
Very interesting, explanations are clear and easy to understand
course is good .But certificate is not available please reverify it once
A really good course for programmers who want to take a bit deeper into CS.
A useful course which introduces key ideas in Approximation Algorithms. Looking forward to part II.
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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.
The course provides a high-level introduction to approximation algorithm. There is no programming assignments but it provides nice introduction to approximation algorithm.
A great course if you want to learn about approximation algorithms from the point of view of linear programming relaxation!
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.
Excellent advanced course! Not for beginner in computer science, nor for people more interested in applying computer science than in theoretical foundations.
This course is awesome. Prof. managed to elaborate the problem and analysis clearly and homework is properly assigned.
This was a relatively easy but well paced introduction to approximation algorithms. I totally enjoyed it.
A useful course which introduces key ideas in Approximation Algorithms. Looking forward to part II.
This class is very clear and easy to understand! Thank you for providing such feast for students!
Fantastic course! Can't wait to do part ii
This course covers the basic ground about approximation algorithms and would be very valuable for a practitioner, but seems designed for people who already know the materials and is more theoretical than applied.
Lectures are very abstract --with a description of the problem and a formal description of the solution--, there are few examples with step-by-step tracing of algorithms --that is how the algorithm actually works to solve the problem--, and a significant amount of expertise is assumed --so much so, that you wouldn't need this course. For example, there are questions about "approximation ratio", but it is not explained. This seems like something that is inherent to approximation algorithms, but is only asked in the exams/project, and is not even mentioned in the lectures. Similar issues abound around the course.
I am a researcher and (in past) an instructor in SDP, Randomized and Approximation Algorithms.
There are a few instances, where things are not explained as well as an advanced UG or a starting Grad student would like, e.g., Knapsack got a bit delirious somewhere in between (the "special special" case, which IMHO was not needed.)
Otherwise, I love Claire's enthusiasm, and the joy she finds in delivering the ideas. She is succinct everywhere (to me).
The assignments could be a bit improved (some are less good, I would personally complain about knapsack), but in general it is a great course, as it gives an accessible introduction to approximation algorithms (for NP-hard problems), which is a very relevant topic, as NP-hard problems are everywhere.
At the time of writing (end summer 2016), it is also a unique course for this very relevant topic.
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!
This class is the one I am seeking for a long time. Theories of Combinatorial optimization and associated approximation algorithms involve lots of hot research topics in machine learning, image processing, and Bioinformatics. The faculty for this course is a leading expert in the related fields.
Excellent Course! I have learnt a lot about Approximation Algorithms in a short span of time.
awesome course!
I'd like to see part 2 and other graduate-level algorithms courses on coursera.
Excellent Course Really helped me to have an in depth knowledge in every concept
A really good course for programmers who want to take a bit deeper into CS.
course is good .But certificate is not available please reverify it once