Academic Profile : Faculty
Assoc Prof CHUA Chek Beng
Associate Professor, School of Physical & Mathematical Sciences - Division of Mathematical Sciences
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Education:
• PhD Cornell University 2003
• MS Cornell University 2002
• BSc(Hons) National University of Singapore 1999
Prof Chua is currently in the School of Physical and Mathematical Sciences since 2006. He received his Bachelor degree in Mathematics and Computational Science from NUS, and Master and Ph.D. degrees from Cornell University. Prior to joining NTU, he was a faculty member at the Department of Combinatorics and Optimization, University of Waterloo. His research interests include Continuous Optimization and Convex Analysis. He has won the SIAM student paper prize in 2003, and has published in top journal such as the SIAM Journal on Optimization and Mathematical Programming.
• PhD Cornell University 2003
• MS Cornell University 2002
• BSc(Hons) National University of Singapore 1999
Prof Chua is currently in the School of Physical and Mathematical Sciences since 2006. He received his Bachelor degree in Mathematics and Computational Science from NUS, and Master and Ph.D. degrees from Cornell University. Prior to joining NTU, he was a faculty member at the Department of Combinatorics and Optimization, University of Waterloo. His research interests include Continuous Optimization and Convex Analysis. He has won the SIAM student paper prize in 2003, and has published in top journal such as the SIAM Journal on Optimization and Mathematical Programming.
Prof. Chua studies the theory of continuous optimization, and develops efficient solution methods for several types of optimization models. He has designed and analyzed interior-point algorithms for semidefinite optimization, symmetric cone optimization and homogeneous cone optimization. He studies the possibility of applying homogeneous cone optimization on various problems where semidefinite optimization models are used. This study is partly driven by the possible reduction in the size when semidefinite optimization models are solved as homogeneous cone optimization problems, hence allowing large-scale problems to be solved via homogeneous cone optimization. He also investigated and proved several properties of the primal-dual central paths for semidefinite optimization and homogeneous cone optimization. These properties are useful in the study of local convergence behaviour of path-following algorithms.
Courses Taught
MH3100 Real Analysis I
MH4701 Mathematical Programming
MH4701 Mathematical Programming