Linear Programming

 

 


 

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E.g., 2016-12-04
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E.g., 2016-12-04
Dec 5th 2016

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.

Average: 6 (1 vote)
Dec 5th 2016

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.

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Sep 6th 2016

Learn how to use optimization methodologies and modeling approaches to effectively analyze data. Optimization is the search for the best and most effective solution. In this mathematics course, we will examine optimization through a Business Analytics lens. You will be introduced to the to the theory, algorithms, and applications of optimization.

Average: 9 (1 vote)
Oct 20th 2014

This course will cover the very basic ideas in optimization. Topics include the basic theory and algorithms behind linear and integer linear programming along with some of the important applications. We will also explore the theory of convex polyhedra using linear programming.

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