Publications and Talks

Publications

    Books

1. H. Sato, Riemannian Optimization and Its Applications, Springer, 2021.
   (A review by Dr. Lena Sembach: Book Reviews, SIAM Review, 64(2), 503–513, 2022)
   (A review by Dr. Anders Linnér: Mathematical Reviews, October, 2022)

2. 情報工学編集委員会 編，理工系の基礎 情報工学，丸善出版，2018. (in Japanese)

    Refereed Papers

1. M. Yamada and H. Sato, Conjugate gradient methods for optimization problems on symplectic Stiefel manifold, IEEE Control Systems Letters, 7,  2719–2724, 2023.

2. H. Sato, Riemannian optimization on unit sphere with p-norm and its applications, Computational Optimization and Applications, 85, 897–935, 2023.

3. H. Sakai, H. Sato, and H. Iiduka, Global convergence of Hager–Zhang type Riemannian conjugate gradient method, Applied Mathematics and Computation, 441, 127685 (12 pages), 2023.

4. H. Sato, Riemannian conjugate gradient methods: General framework and specific algorithms with convergence analyses, SIAM Journal on Optimization, 32(4), 2690–2717, 2022.

5. M. Kawai, H. Sato, and T. Shiohama, Topic model-based recommender systems and their applications to cold start problems, Expert Systems with Applications, 202, 117129 (19 pages), 2022.

6. Y. Yamakawa and H. Sato, Sequential optimality conditions for nonlinear optimization on Riemannian manifolds and a globally convergent augmented Lagrangian method, Computational Optimization and Applications, 81(2), 397–421, 2022.

7. X. Zhu and H. Sato, Cayley-transform-based gradient and conjugate gradient algorithms on Grassmann manifolds, Advances in Computational Mathematics, 47(4), 56 (28 pages), online published.

8. J. Goto and H. Sato, Approximated logarithmic maps on Riemannian manifolds and their applications, JSIAM Letters, 13, 17–20, 2021.

9. X. Zhu and H. Sato, Riemannian conjugate gradient methods with inverse retraction, Computational Optimization and Applications, 77(3), 779–810, 2020.

10. K. Sato, H. Sato, and T. Damm, Riemannian optimal identification method for linear systems with symmetric positive-definite matrix, IEEE Transactions on Automatic Control, 65(11), 4493–4508, 2020.

11. K. Tsuyuzaki, H. Sato, K. Sato, and I. Nikaido, Benchmarking principal component analysis for large-scale single-cell RNA-sequencing, Genome Biology, 21:9 (17 pages), 2020.

12. H. Sato, H. Kasai, and B. Mishra, Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport, SIAM Journal on Optimization, 29(2), 1444–1472, 2019.

13. H. Sato and K. Aihara, Cholesky QR-based retraction on the generalized Stiefel manifold, Computational Optimization and Applications, 72(2), 293–308, 2019.

14. H. Sato and K. Aihara, Riemannian Newton's method on the Grassmann manifold exploiting the quotient structure, Transactions of the Japan Society for Industrial and Applied Mathematics, 28(4), 205–241, 2018. (in Japanese)

15. K. Sato and H. Sato, Structure preserving H^2 optimal model reduction based on the Riemannian trust-region method, IEEE Transactions on Automatic Control, 63(2), 505–512, 2018.

16. K. Aihara and H. Sato, A matrix-free implementation of Riemannian Newton's method on the Stiefel manifold, Optimization Letters, 11(8), 1729–1741, 2017.

17. H. Sato, Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis, Optimization, 66(12), 2211–2231, 2017.

18. H. Sato and K. Sato, Riemannian optimal system identification algorithm for linear MIMO systems, IEEE Control Systems Letters, 1(2), 376–381, 2017.

19. H. Sato, Optimization on Riemannian manifolds and its applications, Bulletin of the Japan Society for Industrial and Applied Mathematics, 27(1), 21–30, 2017. (in Japanese)

20. H. Sato, A Dai–Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions, Computational Optimization and Applications, 64(1), 101–118, 2016.

21. H. Sato and T. Iwai, A new, globally convergent Riemannian conjugate gradient method, Optimization, 64(4), 1011–1031, 2015.

22. H. Sato, Joint singular value decomposition algorithm based on the Riemannian trust-region method, JSIAM Letters, 7, 13–16, 2015.

23. H. Sato and T. Iwai, Optimization algorithms on the Grassmann manifold with application to matrix eigenvalue problems, Japan Journal of Industrial and Applied Mathematics, 31(2), 355–400, 2014.

24. H. Sato and T. Iwai, A Riemannian optimization approach to the matrix singular value decomposition, SIAM Journal on Optimization, 23(1), 188–212, 2013.

    Refereed Conference Proceedings

1. H. Sato, Simple acceleration of the Riemannian steepest descent method with the Armijo condition, Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 3836–3843, 2021.

2. H. Sato and K. Sato, Riemannian gradient-based online identification method for linear systems with symmetric positive-definite matrix, Proceedings of the 58th IEEE Conference on Decision and Control (CDC 2019), 3593–3598, 2019.

3. M. Kawai, T. Shiohama, and H. Sato, Practically feasible recommender systems for cold start problems, Proceedings of 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 103–112, 2018.

4. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic recursive gradient algorithm, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80, 2521–2529, 2018.

5. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Proceedings of Machine Learning Research, 84, 269–278, 2018.

6. M. Kawai, T. Shiohama, and H. Sato, Supervised-topic-model-based hybrid filtering for recommender systems, Proceedings of the 2nd International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD 2017), 266–271, 2017.

7. H. Sato and K. Sato, A new H^2 optimal model reduction method based on Riemannian conjugate gradient method, Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016), 5762–5768, 2016.

8. H. Sato and K. Sato, Riemannian trust-region methods for H^2 optimal model reduction, Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015), 4648–4655, 2015.

9. H. Sato, Riemannian conjugate gradient method for complex singular value decomposition problem, Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), 5849–5854, 2014.

10. H. Sato and T. Iwai, A complex singular value decomposition algorithm based on the Riemannian Newton method, Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013), 2972–2978, 2013.

    Non-refereed Papers and Others

        Please see Japanese version.

Talks

    International Conferences

1. H. Sato, General framework of conjugate gradient methods on Riemannian manifolds, International Workshop on Continuous Optimization, December, 2022. (remote conference) (Invited)

2. H. Sato, Simple acceleration of the Riemannian steepest descent method with the Armijo condition, The 60th IEEE Conference on Decision and Control (CDC 2021), December, 2021. (remote conference)

3. H. Sato, New class of Riemannian conjugate gradient methods, SIAM Conference on Optimization (OP21), July, 2021. (remote conference) (Invited)

4. H. Sato, Conjugate gradient methods on Riemannian manifolds, Oberwolfach mini-workshop Computational Optimization on Manifolds, November, 2020. (remote conference) (Invited)

5. H. Sato and K. Sato, Riemannian gradient-based online identification method for linear systems with symmetric positive-definite matrix, The 58th IEEE Conference on Decision and Control (CDC 2019), Palais des Congrès et des Expositions Nice Acropolis, Nice, France, December, 2019.

6. H. Sato, Riemannian optimization algorithms and their applications, International Conference on Nonlinear Analysis and Convex Analysis—International Conference on Optimization: Techniques and Applications (NACA-ICOTA2019), Future University Hakodate, Hakodate, Japan, August, 2019. (Invited Lecture)

7. M. Kawai, T. Shiohama, and H. Sato, Practically feasible recommender systems for cold start problems, 5th Asia-Pacific World Congress on Computer Science and Engineering 2018 (APWC on CSE 2018), Fiji Marriott Resort Momi Bay, Nadi, Fiji, December, 2018.

8. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic recursive gradient algorithm, The 35th International Conference on Machine Learning (ICML 2018), Stockholmsmässan, Stockholm, Sweden, July, 2018.

9. H. Kasai, H. Sato, and B. Mishra, Stochastic recursive gradient on Riemannian manifolds, Geometry in Machine Learning (GiMLi 2018), Stockholmsmässan, Stockholm, Sweden, July, 2018. (Poster Presentation)

10. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis, The 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), H10 Rubicón Palace, Playa Blanca, Spain, April, 2018. (Poster Presentation)

11. K. Aihara and H. Sato, Solving a Newton equation on the Stiefel manifold with matrix-free Krylov subspace methods, 2018 SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Tokyo, Japan, March, 2018.

12. H. Sato and K. Sato, Riemannian optimal system identification algorithm for linear MIMO systems, The 56th IEEE Conference on Decision and Control (CDC 2017), Melbourne Convention Center, Melbourne, Australia, December, 2017.

13. M. Kawai, T. Shiohama, and H. Sato, Supervised-topic-model-based hybrid filtering for recommender systems, 2nd International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD 2017), ACT CITY Hamamatsu, Hamamatsu, Japan, July, 2017.

14. H. Sato and K. Sato, A new H^2 optimal model reduction method based on Riemannian conjugate gradient method, The 55th IEEE Conference on Decision and Control (CDC 2016), ARIA Resort & Casino, Las Vegas, USA, December, 2016.

15. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic variance reduced gradient on Grassmann manifold, The 9th NIPS Workshop on Optimization for Machine Learning (OPT 2016), Centre Convencions Internacional Barcelona, Barcelona, Spain, December, 2016. (Poster Presentation)

16. H. Kasai, H. Sato, and B. Mishra, Riemannian stochastic variance reduced gradient on Grassmann manifold, The Fifth International Conference on Continuous Optimization (ICCOPT 2016), National Graduate Institute for Policy Studies, Tokyo, Japan, August, 2016.

17. H. Sato and K. Sato, Riemannian trust-region methods for H^2 optimal model reduction, The 54th IEEE Conference on Decision and Control (CDC 2015), Osaka International Convention Center, Osaka, Japan, December, 2015.

18. K. Aihara and H. Sato, Matrix-free Krylov subspace methods for solving a Riemannian Newton equation, 2015 SIAM Conference on Applied Linear Algebra (SIAM LA15), Hyatt Regency Atlanta, Atlanta, USA, October, 2015.

19. H. Sato and K. Aihara, Riemannian Newton's method for optimization problems on the Stiefel manifold, 22nd International Symposium on Mathematical Programming (ISMP 2015), Wyndham Grand Pittsburgh Downtown, Pittsburgh, USA, July, 2015.

20. A. Kitao, T. Shiohama, and H. Sato, Financial news classification based on topographic independent component analysis: Optimization on the Stiefel manifold, 16th Conference of the Applied Stochastic Models and Data Analysis (ASMDA 2015), University of Piraeus, Piraeus, Greece, June, 2015.

21. H. Sato, Riemannian optimization and its applications, Hong Kong-Tokyo Workshop on Scientific Computing, National Institute of Informatics, Tokyo, Japan, April, 2015.

22. H. Sato, Riemannian conjugate gradient method for complex singular value decomposition problem, The 53rd IEEE Conference on Decision and Control (CDC 2014), JW Marriott Los Angeles L.A. LIVE, Los Angeles, USA, December, 2014.

23. H. Sato, Global convergence analysis of several Riemannian conjugate gradient methods, 2014 SIAM Conference on Optimization (SIAM OP14), Town and Country Resort & Convention Center, San Diego, USA, May, 2014.

24. H. Sato, Several matrix computation algorithms based on Riemannian optimization techniques, International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA 2014), Tsukuba International Congress Center EPOCHAL TSUKUBA, Tsukuba, Japan, March, 2014. (Poster Presentation)

25. H. Sato and T. Iwai, A complex singular value decomposition algorithm based on the Riemannian Newton method, The 52nd IEEE Conference on Decision and Control (CDC 2013), Firenze Fiera Congress & Exhibition Center, Florence, Italy, December, 2013.

26. H. Sato, Optimization algorithms on the Grassmann and the Stiefel manifolds with applications to numerical linear algebra, Nanjing-Kyoto Joint Workshop on Algorithms, Optimization and Numerical Analysis 2012, Kyoto University, Kyoto, Japan, March, 2012.

27. H. Sato, Optimization on manifolds, Joint Workshop on Modeling, Systems, and Control 2011, Hanoi University of Science and Technology, Hanoi, Vietnam, March, 2011.

    Domestic Conferences and Awards

        Please see the Japanese version.