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: 12^{th} 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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