École normale supérieure
Approximation Algorithms Part II
École normale supérieure

Approximation Algorithms Part II

Taught in English

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Course

Gain insight into a topic and learn the fundamentals

Claire Mathieu

Instructor: Claire Mathieu

4.8

(44 reviews)

32 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace

Details to know

Assessments

33 quizzes

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There are 4 modules in this course

This module does not study any specific combinatorial optimization problem. Instead, it introduces a central feature of linear programming, duality.

What's included

9 videos11 readings8 quizzes1 peer review

This module uses linear programming duality to design an algorithm for another basic problem, the Steiner forest problem.

What's included

8 videos9 readings8 quizzes1 peer review

This module continues teaching algorithmic applications of linear programming duality by applying it to another basic problem, the facility location problem.

What's included

9 videos10 readings8 quizzes1 peer review

We introduce a generalization of linear programming, semi-definite programming.This module uses semi-definite programming to design an approximation algorithm for another basic problem, the maximum cut problem.

What's included

11 videos12 readings9 quizzes1 peer review

Instructor

Claire Mathieu
École normale supérieure
2 Courses31,368 learners

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4.8

44 reviews

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5

Reviewed on Feb 28, 2018

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Reviewed on Oct 27, 2016

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