Tom M. Ragonneau
Self introduction
I am a Ph.D. candidate in the Department of Applied Mathematics at The Hong Kong Polytechnic University, advised by Dr. Zaikun Zhang and Prof. Xiaojun Chen, and supported by the University Grants Committee (UGC) of Hong Kong, under the Hong Kong Ph.D. Fellowship Scheme (HKPFS, ref. PF18-24698).
Research overview
My research interests include mathematical optimization and its applications, especially methods based on inaccurate information and methods dedicated to derivative-free optimization.
Recent projects
- During the early stage of my Ph.D., I developed PDFO (Powell’s Derivative-Free Optimization solvers), a cross-platform package providing MATLAB and Python interfaces for using late Professor M. J. D. Powell’s derivative-free optimization solvers, including UOBYQA, NEWUOA, BOBYQA, LINCOA, and COBYLA, in a joint work with Zaikun Zhang.
- My most recent work is COBYQA, a derivative-free derivative-free trust-region SQP optimization solver for constrained optimization using quadratic approximations. It is implemented in Python, but I plan to develop a Fortran version of the software in the future.
Selected publications
[1] R. Benshila, G. Thoumyre, M. Al Najar, G. Abessolo Ondoa, R. Almar, E. Bergsma, G. Hugonnard, L. Labracherie, B. Lavie, T. M. Ragonneau, S. Ehouarn, B. Vieublé, and D. Wilson (2020). A deep learning approach for estimation of the nearshore bathymetry. J. Coast. Res., 95(sp1), 1011–1015.