Friday, July 14

Interior-point Methods and Semidefinite Programming

8:30 AM-9:15 AM
Room: Rio Mar 5
Chair: Juan Meza, Sandia National Laboratories and Department of Energy, USA

Research on interior-point methods has been one of the most active and fruitful areas in optimization, and interior-point methods have proven to be a powerful approach for solving problems with many inequality constraints. In theory, strong complexity and convergence results exist for interior-point methods when applied to convex optimization problems. In practice, they have found great success in solving linear and semidefinite programming problems, as well as other convex optimization problems. A strong influence of interior-point methodology is also evident in many recently proposed algorithms for nonconvex optimization problems. In this presentation, the speaker will provide an overview of interior-point methodology from an algorithmic viewpoint and use semidefinite programming in demonstrating the elegance, power, applicability, as well as the limitations of interior-point methods.

Yin Zhang
Computational and Applied Mathematics
Rice University, USA
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