Tom M. Ragonneau
I am a fresh Ph.D. graduate from the Department of Applied Mathematics at The Hong Kong Polytechnic University. I was 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).
My research interests include mathematical optimization and its applications, especially
- methods dedicated to derivative-free optimization, and
- methods based on inaccurate information.
 T. M. Ragonneau and Z. Zhang. PDFO: A Cross-Platform Package for Powell’s Derivative-Free Optimization Solver. 2023. arXiv:2302.13246 [math.OC].
 T. M. Ragonneau and Z. Zhang. An Optimal Interpolation Set for Model-Based Derivative-Free Optimization Methods. 2023. arXiv:2302.09992 [math.OC].
 T. M. Ragonneau. “Model-Based Derivative-Free Optimization Methods and Software.” Ph.D. thesis. Hong Kong: Department of Applied Mathematics, The Hong Kong Polytechnic University, 2022.
 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. “A deep learning approach for estimation of the nearshore bathymetry.” J. Coast. Res. 95.sp1. (2020), pp. 1011–1015.
Ph.D. in Computational Mathematics
- Supervised by Dr. Zaikun Zhang and co-supervised by Prof. Xiaojun Chen.
- Support was provided by the University Grants Committee (RGC) of Hong Kong, under the Hong Kong Ph.D. Fellowship Scheme (HKPFS, ref. PF18-24698).
- Thesis titled “Model-Based Derivative-Free Optimization Methods and Software.”
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.
- Initial development of the PDFO package.