Approximation Algorithms Part II
Part 2 of the approximation algorithms
This is a continuation of Part 1 of Approximation Algorithms. You'll learn how to use linear programming duality to the design of several approximation algorithms, as well as semidefinite programming to Maxcut. You will be introduced to a variety of issues at the foundations of theoretical computer science, as well as sophisticated design and analytic tools, by studying both portions of this course.
When presented with a novel combinatorial optimization issue, you will be able to detect whether it is similar to one of a few known fundamental problems, and you will be able to create linear programming relaxations and utilize randomized rounding to try to solve your own problem. The material of the course, particularly the homework, is theoretical in nature, with no programming tasks. This is the second half of a two-part Approximation Algorithms course.
- Flexible deadlines: Reset deadlines based on your availability.
- 100% online: Start now and learn at times that suit you.
- Approx. 33 hours to complete
- Subtitles: French, Portuguese (European), Russian, English, Spanish
Rating: 4.8/5
Enroll here: coursera.org/learn/approximation-algorithms-part-2
