Publications

Refereed Papers in Journals

  1. S. Takahashi, M. Tanaka, and S. Ikeda:
    Blind deconvolution with non-smooth regularization via Bregman proximal DCAs,
    Signal Processing, 202 (2023), 108734.
  2. 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).
  2. S. Takahashi and A. Takeda:
    Approximate Bregman proximal gradient algorithm for relatively smooth nonconvex optimization,
    arXiv:2311.07847 (2023).

Conferences

  1. 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.
  2. S. Takahashi:
    Bregman proximal DC algorithms and their application to blind deconvolution with nonsmooth regularization,
    International Workshop on Continuous Optimization, online, December 2022.
  3. 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.
  4. 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.
  5. 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)

(C) 2020 Shota Takahashi