| Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport H Sato, H Kasai, B Mishra SIAM Journal on Optimization 29 (2), 1444-1472, 2019 | 152* | 2019 |
| A new, globally convergent Riemannian conjugate gradient method H Sato, T Iwai Optimization 64 (4), 1011-1031, 2015 | 134 | 2015 |
| Benchmarking principal component analysis for large-scale single-cell RNA-sequencing K Tsuyuzaki, H Sato, K Sato, I Nikaido Genome Biology 21 (1), 9, 2020 | 101 | 2020 |
| A Dai–Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions H Sato Computational Optimization and Applications 64 (1), 101-118, 2016 | 76 | 2016 |
| Riemannian Optimization and Its Applications H Sato Springer, 2021 | 69 | 2021 |
| A Riemannian optimization approach to the matrix singular value decomposition H Sato, T Iwai SIAM Journal on Optimization 23 (1), 188-212, 2013 | 60 | 2013 |
| Riemannian stochastic recursive gradient algorithm H Kasai, H Sato, B Mishra International Conference on Machine Learning, 2516-2524, 2018 | 52 | 2018 |
| Riemannian conjugate gradient methods: General framework and specific algorithms with convergence analyses H Sato SIAM Journal on Optimization 32 (4), 2690-2717, 2022 | 44 | 2022 |
| Structure-Preserving Optimal Model Reduction Based on the Riemannian Trust-Region Method K Sato, H Sato IEEE Transactions on Automatic Control 63 (2), 505-512, 2017 | 37 | 2017 |
| Riemannian conjugate gradient methods with inverse retraction X Zhu, H Sato Computational Optimization and Applications 77 (3), 779-810, 2020 | 32 | 2020 |
| Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis H Sato Optimization 66 (12), 2211-2231, 2017 | 30* | 2017 |
| Riemannian trust-region methods for H2 optimal model reduction H Sato, K Sato 2015 54th IEEE Conference on Decision and Control (CDC), 4648-4655, 2015 | 29 | 2015 |
| Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis H Kasai, H Sato, B Mishra International Conference on Artificial Intelligence and Statistics, 269-278, 2018 | 25 | 2018 |
| Cholesky QR-based retraction on the generalized Stiefel manifold H Sato, K Aihara Computational Optimization and Applications 72 (2), 293-308, 2019 | 24 | 2019 |
| Optimization algorithms on the Grassmann manifold with application to matrix eigenvalue problems H Sato, T Iwai Japan Journal of Industrial and Applied Mathematics 31 (2), 355-400, 2014 | 23 | 2014 |
| Topic model-based recommender systems and their applications to cold-start problems M Kawai, H Sato, T Shiohama Expert Systems with Applications 202, 117129, 2022 | 20 | 2022 |
| A matrix-free implementation of Riemannian Newton’s method on the Stiefel manifold K Aihara, H Sato Optimization Letters 11 (8), 1729-1741, 2017 | 18 | 2017 |
| Riemannian conjugate gradient method for complex singular value decomposition problem H Sato 53rd IEEE Conference on Decision and Control, 5849-5854, 2014 | 15 | 2014 |
| Joint singular value decomposition algorithm based on the Riemannian trust-region method H Sato JSIAM Letters 7, 13-16, 2015 | 14 | 2015 |
| A complex singular value decomposition algorithm based on the Riemannian Newton method H Sato, T Iwai 52nd IEEE Conference on Decision and Control, 2972-2978, 2013 | 14 | 2013 |
Hiroyuki KASAIWASEDA University, JAPAN在 waseda.jp 的电子邮件经过验证
