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Approximation Algorithms Part II

Approximation algorithms, Part 2 This is the continuation of Approximation algorithms, Part 1. Here you will learn linear programming duality applied to the design of some approximation algorithms, and semidefinite programming applied to Maxcut. By taking the two parts of 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 second of a two-part course on Approximation Algorithms.

Status: Mathematical Modeling
Status: Theoretical Computer Science
Course36 hours

Featured reviews

AP

5.0Reviewed Oct 27, 2016

Demanding course with lots of great algorithm concepts based on Linear Programming.

RA

5.0Reviewed Mar 13, 2016

It is remarkable to note that Professor Claire Mathieu explains such a complex subject in such a elegant and understandable manner.

PV

5.0Reviewed Feb 15, 2017

Even better than the first! Very good classes (except for the two first of week 3 ...)

All reviews

Showing: 9 of 9

Andrew Panufnik
5.0
Reviewed Oct 28, 2016
Deleted Account
5.0
Reviewed Mar 1, 2018
Claus D. Makowka, PhD
2.0
Reviewed Aug 18, 2016
Maxime Jaubert
5.0
Reviewed Feb 26, 2017
Zhouningnan
5.0
Reviewed Jan 10, 2017
Refik Arkut
5.0
Reviewed Mar 14, 2016
Paulo Emílio de Vilhena
5.0
Reviewed Feb 16, 2017
Reynaldo Gil-Pons
5.0
Reviewed Mar 4, 2016
victor guillot
4.0
Reviewed Jul 21, 2017