Monday, May 10

Semidefinite Programming

10:45 AM-12:45 PM
Room: Georgia 1 & 2

Semidefinite Programming (SDP) is an extension of linear programming in Euclidean space to the space of real symmetric matrices. Primal-dual interior-point methods developed for linear programs have been successfully extended to SDPs, and SDPs have attracted much attention in various fields such as control system engineering, global optimization, combinatorial optimization, and truss topology design. The speakers in this minisymposium will discuss theoretical and algorithmic issues in interior-point methods for SDPs and semidefinite programming relaxation of nonconvex programs.

Organizer: Masakazu Kojima
Tokyo Institute of Technology, Japan

10:45-11:10 Search Directions for Primal-Dual Interior Point Methods in Semidefinite Programming
Kim-Chuan Toh, National University of Singapore, Singapore
11:15-11:40 Incomplete Orthogonalization Preconditioner for Solving Large and Dense Linear Systems which Arise from Semidefinite Programming
Shao-Liang Zhang, University of Tokyo, Japan; Masakazu Kojima, Organizer; and Kazuhide Nakata, Nihon Sun Micro Systems, Tokyo, Japan
11:45-12:10 Spectral Relations for Symmetric Cones and Their Use in Interior Point Methods
Jos F. Sturm, Maastricht University, The Netherlands
12:15-12:40 Discretization and Localization in Successive Convex Relaxation Methods for Nonconvex Quadratic Optimization Problems
Masakazu Kojima, Organizer; and Levent Tuncel, University of Waterloo, Canada

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