Convex Optimization

There is a great race under way to determine which important problems can be posed in a convex setting.
- Jon Dattorro, Stanford.
Conduction By: Udayan Kanade

Abstract: The theory of optimization of convex problems is well developed, and widely applicable to problems in engineering and finance. “Convex Optimization” includes – and is a generalization of – important optimization categories such as least squares, linear programming, geometric programming and entropy maximization.

In this course, we will study convex optimization problems, their mathematical analysis, and algorithms to solve them.

Target Audience: Engineers of any stream, operations research folks, finance people. Mathematicians would be interested in the differential geometry and analysis aspects.

Course Topics: Optimization theory, convex optimization, Lagrangian duality, interior point methods.

Prerequisites: 12th std. mathematics, and simple linear algebra.

Teacher's Introduction: Udayan Kanade works for Oneirix Labs, an engineering research company. He has an MS in Computer Science from Stanford. He has taught many courses, but this is the first time that he is teaching convex optimization.

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Attending Definitely

Format: 5 lectures x 2 hours
Date: 28th Dec 2008 (first lecture)
Time: 3:00 pm – 5:00 pm
Place: Jnana Prabodhini 4th floor
Prerequisites: 12th std maths & linear algebra