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.
Problem definition
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Skills You'll Learn
Algorithms, Theoretical Computer Science, Solution Design, Probability, Operations Research, Mathematical Modeling, Linear Algebra, Combinatorics
Reviews
4.8 (44 ratings)
- 5 stars88.63%
- 4 stars6.81%
- 3 stars2.27%
- 2 stars2.27%
PV
Feb 16, 2017
Even better than the first! Very good classes (except for the two first of week 3 ...)
AP
Oct 28, 2016
Demanding course with lots of great algorithm concepts based on Linear Programming.
From the lesson
Steiner Forest and Primal-Dual Approximation Algorithms
This module uses linear programming duality to design an algorithm for another basic problem, the Steiner forest problem.
Taught By
Claire Mathieu