Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization. Submitted, 2022. [pdf]
Hiroki Tanabe., Ellen H. Fukuda, and Nobuo Yamashita: Convergence rates analysis of a multiobjective proximal gradient method, Optimization Letters, 2022. [doi | pdf]
Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: New merit functions for multiobjective optimization and their properties. Submitted, 2022. [pdf]
Hiroki Tanabe., Ellen H. Fukuda, and Nobuo Yamashita: An accelerated proximal gradient method for multiobjective optimization, Submitted, 2022. [pdf]
Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: Proximal gradient methods for multiobjective optimization and their applications, Computational Optimization and Applications, 72(2), pp. 339–361, 2019. [doi | pdf]
国際会議発表
Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: An accelerated proximal gradient method for multiobjective optimization, SIAM Conference on Optimization 2021, Online, July 2021.
Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita: Multiobjective proximal gradient methods and application to robust multiobjective optimization, 17th EUROPT Workshop on Advances in Continuous Optimization, No. 98, Glasgow UK, June 2019.
Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita: Merit functions for nonlinear multiobjective optimization and convergence rates analysis of proximal gradient methods, 30th European Conference on Operational Research, No. 1864, Dublin Ireland, June 2019.
Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita: A proximal gradient method for multiobjective optimization and application to robust multiobjective optimization, Society of Instrument and Control Engineers Annual Conference 2018, No. 128, Nara Japan, September 2018. 【若手論文賞ファイナリスト】