Publications

Publications

  1. 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]
  2. Hiroki Tanabe., Ellen H. Fukuda, and Nobuo Yamashita: Convergence rates analysis of a multiobjective proximal gradient method, Optimization Letters, 2022. [doi | pdf]
  3. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: New merit functions for multiobjective optimization and their properties. Submitted, 2022. [pdf]
  4. Hiroki Tanabe., Ellen H. Fukuda, and Nobuo Yamashita: An accelerated proximal gradient method for multiobjective optimization, Submitted, 2022. [pdf]
  5. 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]

International Conferences

  1. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: An accelerated proximal gradient method for multiobjective optimization, SIAM Conference on Optimization 2021, Online, July 2021.
  2. 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.
  3. 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.
  4. 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. [A finalist for the Young Author’s Award]

Domestic Conferences

  1. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: On the generalization of stepsizes for the accelerated proximal gradient method for multiobjective optimization and the convergence of iterates (多目的最適化問題に対する加速付き近接勾配法におけるステップ幅の一般化と点列の収束性について), Operations Research Society of Japan – Spring Conference, online, March 2022.
  2. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: An accelerated proximal gradient method for multiobjective optimization (多目的最適化問題に対する加速付き近接勾配法), Operations Research Society of Japan – Spring Conference, online, March 2021.
  3. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: New merit functions for multiobjective optimization (多目的最適化問題に対する新しいメリット関数), 15th Kyoto University ICT Innovation, online, February 2021. [Poster session]
  4. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: Various merit functions for multiobjective optimization and efficient method for computing the merit functions involving l_1 regularization terms (多目的最適化問題に対する様々なメリット関数とl_1正則化項を含んだ多目的最適化問題に対するメリット関数の効率的な計算方法), Operations Research Society of Japan – Spring Conference, Nara, March 2020.
  5. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: New algorithm for multiobjective optimization: proximal gradient methods (多目的最適化問題に対する新たな解法: 多目的近接勾配法), 14th Kyoto University ICT Innovation, Kyoto, February 2020. [Poster session, Excellent Research Award]
  6. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: Merit functions for multiobjective optimization and various analyses using them (多目的最適化問題に対するメリット関数とそれを用いた様々な解析), Operations Research Society of Japan – Spring Conference, Chiba, March 2019.
  7. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: On proximal gradient methods for multiobjective optimization and their convergence rates (多目的最適化問題に対する近接勾配法とその収束速度について), SSOR2018 Kansai Region, Nara, November 2018.
  8. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: Proximal gradient methods for multiobjective optimization and their applications (多目的最適化問題における近接勾配法とその応用), Workshop on Optimization and its Applications: Young Researchers Meeting 2018, Tsukuba, June 2018.
  9. Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita: A proximal gradient method for multiobjective optimization and its applications (多目的最適化問題に対する近接勾配法とその応用), Optimization: Modeling and Algorithms, Tokyo, March 2018.

Awards

  1. Student Thesis Award 2020, from Operations Research Society of Japan. [given to 6 students]
  2. Full Exemption from Repayment for Graduate School Students with Particularly Outstanding Achievements for Category 1 Loans, from Japan Student Services Organization. [1,456 out of 21,538]
  3. Grand Prize in Data Analytics Competition 2019 on Japan Institute of Marketing Science. [1 out of 14 teams]
  4. Excellent Research Award at Kyoto University’s 14th ICT Innovation. [8 out of 54]
  5. A Finalist for the Young Author’s Award in Society of Instrument and Control Engineers Annual Conference 2018.

Books

  1. Takashi Someda, Naoki Kitora, Ippei Usami, Ryuji Masui, and Hiroki Tanabe: An Introduction to Sparse Modeling for IT Engineers (ITエンジニアのためのスパースモデリング入門), Shoeisha, July 2021.

Invited Talks

  1. Takeshi Koshizuka, Kohei Harada, Yoshiaki Inoue, Ayumi Igarashi, Hiroki Tanabe, and Kiyohito Nagano, Open Roundtable: The Present and Future of OR Research (公開座談会 ~OR研究の現在と未来~), Operations Research Society of Japan, 2022.
タイトルとURLをコピーしました