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On Methods for Solving Nonlinear Semidefinite Optimization Problems

The nonlinear semidefinite optimization problem arises from applications in system control, structural design, financial management, and other fields. However, much work is yet to be done to effectively solve this problem. We introduce some new theoretical and algorithmic development in this field. In particular, we discuss first and second-order algorithms that appear to be promising, which include the alternating direction method, the augmented Lagrangian method, and the smoothing Newton method. Convergence theorems are presented and preliminary numerical results are reported.
Type of Seminar:
Optimization and Applications Seminar
Department of Decision Sciences, School of Business, National University of Singapore
Nov 23, 2009   16:30-18:00

ETH Zentrum, Rämistrasse 101, HG G 19.1
Contact Person:

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Biographical Sketch:
Provost’s Chair Professor, National University of Singapore