Hi, I'm Kangming Chen
A lounging cat
I am Kangming Chen, originally from China, and obtained my doctoral degree from the System Optimization Lab at Kyoto University, where I was supervised by Prof. Ellen Hidemi Fukuda. I am dedicated to advancing research in optimization, computational mathematics, and their applications in fields such as artificial intelligence, machine learning, and operations research.
Publications
You can also find my articles on my Google Scholar profile.
Journal Publications
Riemannian conditional gradient methods for composite optimization problems
K. Chen, E. H. Fukuda
Computational and Applied Mathematics, 45:151
Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds with retraction and vector transport
K. Chen, E. H. Fukuda, and H. Sato
Applied Mathematics and Computation, vol. 486, p. 129001
A proximal gradient method with Bregman distance in multi-objective optimization
K. Chen, E. H. Fukuda and N. Yamashita
Pacific Journal of Optimization. 20(4): 809-826
Preprints / Working Papers
A proximal gradient framework for composite multiobjective optimization on Riemannian manifolds
K. Chen
Submitted
An inertial iteratively regularized extragradient method for bilevel variational inequality problems
M. M. Alves, K. Chen, E. H. Fukuda
Submitted
Talks & Presentations
An inertial iteratively regularized extragradient method for bilevel variational inequality problems
Proximal Gradient Method for Multi-Objective Optimization with Bregman Distance
Riemannian generalized conditional gradient methods
Generalized Conditional Gradient Method with Three Step Size Strategies on Riemannian Manifolds
Riemannian conditional gradient methods for composite optimization problems
Multiobjective Proximal gradient methods on Riemannian manifolds
Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds
Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds
Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds
A proximal gradient method with Bregman distance in multi-objective optimization
Background
Education
Ph.D. in Applied Mathematics and Physics
Kyoto University
2026M.S. in Applied Mathematics and Physics
Kyoto University
2022B.S. in Mathematics and Applied Mathematics
Beijing University of Chemical Technology
2018Grants
Kyoto University Division of Graduate Studies SPRING Program
JST
2024/04 – 2025/03Kyoto University Science and Technology Innovation Creation Fellowship
JST
2022/04 – 2024/03Service
President
Kyoto University SIAM Student Chapter
2025/04 – 2026/03Vice-president
Kyoto University SIAM Student Chapter
2023/04 – 2025/03Member
Operations Research Society of Japan (ORSJ)
2023/07 – presentMember
Japan Society for Industrial and Applied Mathematics (JSIAM)
2024 – present