Date:
Monday, January 29, 2007.
4:15 PM.

Location: Bldg 380, Room 380C (basement)
Refreshments served at 4:00PM in the courtyard outside Room 380C

<p>In this talk I will give an overview of general convex</p> <p>optimization,</p> <p>which can be thought of as an extension of linear programming,</p> <p>and some recently developed subfamilies such as second-order cone,</p> <p>semidefinite, and geometric programming. Like linear programming,</p> <p>we have a fairly complete duality theory, and</p> <p>very effective numerical methods for these problem classes;</p> <p>in addition, recently developed software tools considerably</p> <p>reduce the effort of specifying and solving convex optimization</p> <p>problems.</p> <p>There is a steadily expanding list of new applications of convex</p> <p>optimization, in areas such as circuit design, signal processing,</p> <p>statistics, machine learning, communications, control, finance,</p> <p>and other fields. Convex optimization is also emerging as an</p> <p>important tool for hard, non-convex problems, where it can be used to</p> <p>generate lower bounds on the optimal value, and as a heuristic method</p>