Tom M. Ragonneau

Actively developing software for derivative-free optimization.

Self introduction

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).

Research overview

My research interests include mathematical optimization and its applications, especially methods based on inaccurate information and methods dedicated to derivative-free optimization.

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. “A deep learning approach for estimation of the nearshore bathymetry.” J. Coast. Res. 95.sp1. (2020), pp. 1011–1015.

[2] T. M. Ragonneau. “Model-Based Derivative-Free Optimization Methods and Software.” Ph.D. thesis. HK: Department of Applied Mathematics, The Hong Kong Polytechnic University, 2022.


Ph.D. student in computational mathematics


M.Sc. in scientific computing

Toulouse INP, ENSEEIHT · Toulouse, France

M.Eng. in computer science and applied mathematics

Toulouse INP, ENSEEIHT · Toulouse, France
  • Graduated in High Performance Computing and Big Data.


Research associate

  • Pursued the development of COBYQA.

Teaching assistant


Revision Tutorial Sessions (RTS) for

  • AMA1110 Basic Mathematics I – Calculus and Probability & Statistics.
  • AMA1120 Basic Mathematics II – Calculus and Linear Algebra.

Research assistant

  • Initiated the development of PDFO.

Recent posts

Kantorovich inequality

Proofs of the Kantorovich inequality.
4 min read