Tom M. Ragonneau
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).
- 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.
 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.
Ph.D. student in computational mathematics
M.Sc. in scientific computing
- Graduated in Performance in Software, Media and Scientific Computing.
M.Eng. in computer science and applied mathematics
- Graduated in High Performance Computing and Big Data.