Hiroyuki Sato
Hiroyuki Sato
Associate Professor, Ritsumeikan University
Verified email at - Homepage
Cited by
Cited by
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
A new, globally convergent Riemannian conjugate gradient method
H Sato, T Iwai
Optimization 64 (4), 1011-1031, 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
A Dai–Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions
H Sato
Computational Optimization and Applications 64 (1), 101-118, 2016
Riemannian Optimization and Its Applications
H Sato
Springer, 2021
A Riemannian optimization approach to the matrix singular value decomposition
H Sato, T Iwai
SIAM Journal on Optimization 23 (1), 188-212, 2013
Riemannian stochastic recursive gradient algorithm
H Kasai, H Sato, B Mishra
International Conference on Machine Learning, 2516-2524, 2018
Riemannian conjugate gradient methods: General framework and specific algorithms with convergence analyses
H Sato
SIAM Journal on Optimization 32 (4), 2690-2717, 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
Riemannian conjugate gradient methods with inverse retraction
X Zhu, H Sato
Computational Optimization and Applications 77 (3), 779-810, 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
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
Cholesky QR-based retraction on the generalized Stiefel manifold
H Sato, K Aihara
Computational Optimization and Applications 72 (2), 293-308, 2019
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
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
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
A matrix-free implementation of Riemannian Newton’s method on the Stiefel manifold
K Aihara, H Sato
Optimization Letters 11 (8), 1729-1741, 2017
Riemannian conjugate gradient method for complex singular value decomposition problem
H Sato
53rd IEEE Conference on Decision and Control, 5849-5854, 2014
Joint singular value decomposition algorithm based on the Riemannian trust-region method
H Sato
JSIAM Letters 7, 13-16, 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
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