Course details of CS 709 - Convex Optimization

Course Name Convex Optimization
Total Credits 6
Type T
Lecture 6
Tutorial 0
Practical 0
Selfstudy 0
Half Semester N
Prerequisite 0
Text Reference [1] R.T.Rockafellar. Convex Analysis. Princeton University Press, 1996. [2] S.Boyd and L.Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Available at [3] A.Nemirovski. Lectures On Modern Convex Optimization (2005). Available at [4] Y.Nesterov. Introductory Lectures on Convex Optimization: A Basic Course. Kluwer Academic Publishers, 2004.
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
Last Update 08-07-2014 09:22:42.156971