34 citations · 227 across the 31 of their papers we have counts for
31 papers
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization Problems
Dai Hai Nguyen, Tetsuya Sakurai
We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions. The objective is composed of two functi…
Contour Integral-based Quantum Algorithm for Estimating Matrix Eigenvalue Density
Yasunori Futamura, Xiucai Ye, Tetsuya Sakurai
The eigenvalue density of a matrix plays an important role in various types of scientific computing such as electronic-structure calculations. In this paper, we propose a quantum a…
A Parallel Computing Method for the Higher Order Tensor Renormalization Group
Takumi Yamashita, Tetsuya Sakurai
In this paper, we propose a parallel computing method for the Higher Order Tensor Renormalization Group (HOTRG) applied to a -dimensional simple lattice model. Se…
Complex moment-based method with nonlinear transformation for computing large and sparse interior singular triplets
Akira Imakura, Tetsuya Sakurai
This paper considers computing interior singular triplets corresponding to the singular values in some interval. Based on the concept of the complex moment-based parallel eigensolv…
LSEC: Large-scale spectral ensemble clustering
Hongmin Li, Xiucai Ye, Akira Imakura +1
Ensemble clustering is a fundamental problem in the machine learning field, combining multiple base clusterings into a better clustering result. However, most of the existing metho…
Divide-and-conquer based Large-Scale Spectral Clustering
Hongmin Li, Xiucai Ye, Akira Imakura +1
Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited comp…
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Akira Imakura, Anna Bogdanova, Takaya Yamazoe +2
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications. A collaborative data analysis through sharing…
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Anna Bogdanova, Akie Nakai, Yukihiko Okada +2
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an…
Interpretable collaborative data analysis on distributed data
Akira Imakura, Hiroaki Inaba, Yukihiko Okada +1
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distr…
Flexible subspace iteration with moments for an effective contour integration-based eigensolver
Sarah Huber, Yasunori Futamura, Martin Galgon +3
Contour integration schemes are a valuable tool for the solution of difficult interior eigenvalue problems. However, the solution of many large linear systems with multiple right h…
A Parallel Computing Method for the Coupled-Cluster Singles and Doubles
Takumi Yamashita, Taichi Kosugi, Yu-ichiro Matsushita +1
In this paper, we present a parallel computing method for the coupled-cluster singles and doubles (CCSD) in periodic systems. The CCSD in periodic systems solves simultaneous equat…
Multiclass spectral feature scaling method for dimensionality reduction
Momo Matsuda, Keiichi Morikuni, Akira Imakura +2
Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information…
Data collaboration analysis for distributed datasets
Akira Imakura, Tetsuya Sakurai
In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models re…
Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module
Yusei Miura, Tetsuya Sakurai, Claus Aranha +3
Recently, deep convolutional neural networks have shown good results for image recognition. In this paper, we use convolutional neural networks with a finder module, which discover…
Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects
Andreas Alvermann, Achim Basermann, Hans-Joachim Bungartz +30
We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II…
Spectral feature scaling method for supervised dimensionality reduction
Momo Matsuda, Keiichi Morikuni, Tetsuya Sakurai
Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the…
Efficient and Scalable Calculation of Complex Band Structure using Sakurai-Sugiura Method
Shigeru Iwase, Yasunori Futamura, Akira Imakura +2
Complex band structures (CBSs) are useful to characterize the static and dynamical electronic properties of materials. Despite the intensive developments, the first-principles calc…
Contour integral method for obtaining the self-energy matrices of electrodes in electron transport calculations
Shigeru Iwase, Yasunori Futamura, Akira Imakura +3
We propose an efficient computational method for evaluating the self-energy matrices of electrodes to study ballistic electron transport properties in nanoscale systems. To reduce…
Efficient calculation of electronic structure using O(N) density functional theory
Ayako Nakata, Yasunori Futamura, Tetsuya Sakurai +2
We propose an efficient way to calculate the electronic structure of large systems by combining a large-scale first-principles density functional theory code, Conquest, and an effi…
Reduced-Shifted Conjugate-Gradient Method for a Green's Function: Efficient Numerical Approach in a Nano-structured Superconductor
Yuki Nagai, Yasushi Shinohara, Yasunori Futamura +1
We propose the reduced-shifted Conjugate-Gradient (RSCG) method, which is numerically efficient to calculate a matrix element of a Green's function defined as a resolvent of a Hami…
Solving large-scale nonlinear eigenvalue problems by rational interpolation approach and resolvent sampling based Rayleigh-Ritz method
Jinyou Xiao, Chuanzeng Zhang, Tsung-Ming Huang +1
Numerical solution of nonlinear eigenvalue problems (NEPs) is frequently encountered in computational science and engineering. The applicability of most existing methods is limited…
Alternating optimization method based on nonnegative matrix factorizations for deep neural networks
Tetsuya Sakurai, Akira Imakura, Yuto Inoue +1
The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objectiv…
Stochastic Estimation of Nuclear Level Density in the Nuclear Shell Model: An Application to Parity-Dependent Level Density in Ni
Noritaka Shimizu, Yutaka Utsuno, Yasunori Futamura +3
We introduce a novel method to obtain level densities in large-scale shell-model calculations. Our method is a stochastic estimation of eigenvalue count based on a shifted Krylov-s…
A map of contour integral-based eigensolvers for solving generalized eigenvalue problems
Akira Imakura, Lei Du, Tetsuya Sakurai
Recently, contour integral-based methods have been actively studied for solving interior eigenvalue problems that find all eigenvalues located in a certain region and their corresp…