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Course Information
Identification

CS 709: Convex Optimization
 
Description

This is primarily an introductory course on convex optimization. The focus however is on topics
which might be useful for machine learning and computer vision researchers. Accordingly, some
advanced/specialized topics are included:

1. Theory
• Convex Analysis: Convex Sets, Convex Functions, Calculus of convex functions
• Optimality of Convex Programs: 1st order nec. and suff. conditions, KKT conditions
• Duality: Lagrange and Conic duality
2. Standard Convex Programs and Applications
• Linear and Quadratic Programs
• Conic Programs: QCQPs, SOCPs, SDPs.
3. Optimization Techniques
• Smooth Problems: (proj.) Gradient descent, Nesterov's accelerated method, Newton's
methods
• Non­smooth Problems: (proj.) Sub­gradient descent
• Special topics: Active set and cutting planes methods
 
References

[1] R.T.Rockafellar. Convex Analysis. Princeton University Press, 1996.

[2] S.Boyd and L.Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
Available at http://www.stanford.edu/~boyd/cvxbook/

[3] A.Nemirovski. Lectures On Modern Convex Optimization (2005). Available at
www2.isye.gatech.edu/~nemirovs/Lect_ModConvOpt.pdf

[4] Y.Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer Academic
Publishers, 2004.
 
Home Page

http://www.cse.iitb.ac.in/saketh/teaching/cs709.html
 
Prerequisites

Basic courses on Linear Algebra and Calculus
 
Other Details

Duration : Full Semester Total Credit : 6
Type : Theory
 
Current Semester (Autumn 2017-18)

Status : Not Offered Instructor : ---
 
Next Semester (Spring 2017-18)

Status : Offered Instructor : Prof. Ganesh Ramakrishnan




Last Modified Date: 09-May-2016

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