Publications

• Refereed Papers in Journals
• Preprints
• Conferences

Refereed Papers in Journals

1. S. Takahashi and A. Takeda:
   Approximate Bregman proximal gradient algorithm for relatively smooth nonconvex optimization,
   To appear in Computational Optimization and Applications.
2. S. Takahashi, M. Tanaka, and S. Ikeda:
   Blind deconvolution with non-smooth regularization via Bregman proximal DCAs,
   Signal Processing, 202 (2023), 108734.
3. S. Takahashi, M. Fukuda, and M. Tanaka:
   New Bregman proximal type algorithms for solving DC optimization problems,
   Computational Optimization and Applications, 83 (2022), 893–931.
   [codes]

Preprints

1. S. Takahashi, M. Tanaka, and S. Ikeda:
   Majorization-minimization Bregman proximal gradient algorithms for nonnegative matrix factorization with the Kullback–Leibler divergence,
   arXiv:2405.11185 (2024).

Conferences

1. S. Takahashi and A. Takeda:
   Approximate Bregman proximal gradient algorithm for relatively smooth nonconvex optimization,
   The 25th International Symposium on Mathematical Programming, Montreal, Canada, July 2024.
   (Session Organizer, Gradient-based methods: theory and advances)
2. S. Takahashi:
   Bregman proximal DC algorithms and their application to blind deconvolution with nonsmooth regularization,
   2023 SIAM Conference on Optimization, Seattle, Washington, U.S., May 2023.
3. S. Takahashi:
   Bregman proximal DC algorithms and their application to blind deconvolution with nonsmooth regularization,
   International Workshop on Continuous Optimization, online, December 2022.
4. S. Takahashi:
   Bregman proximal DC algorithms and their application to blind deconvolution with nonsmooth regularization,
   The 6th RIKEN-IMI-ISM-NUS-ZIB-MODAL-NHR Workshop on Advances in Classical and Quantum Algorithms for Optimization and Machine Learning, Tokyo, Japan, September 2022.
5. S. Takahashi, M. Fukuda, and M. Tanaka:
   New Bregman proximal type algorithms for solving DC optimization problems,
   EUROPT 2021 18th Workshop on Advances in Continuous Optimization, online, July 2021.
6. S. Takahashi, and M. Fukuda:
   New Bregman proximal type algorithms for solving DC optimization problems,
   INFORMS Optimization Society Conference 2020, Greenville, South Carolina, U.S., March 2020.
   (cancelled due to COVID-19)