5 citations · 8 across the 9 of their papers we have counts for
9 papers
A fast and accurate computation method for reflective diffraction simulations
Shuhei Kudo, Yusaku Yamamoto, Takeo Hoshi
We present a new computation method for simulating reflection high-energy electron diffraction and the total-reflection high-energy positron diffraction experiments. The two experi…
Performance prediction of massively parallel computation by Bayesian inference
Hisashi Kohashi, Harumichi Iwamoto, Takeshi Fukaya +2
A performance prediction method for massively parallel computation is proposed. The method is based on performance modeling and Bayesian inference to predict elapsed time T as a fu…
A Parallelizable Energy-Preserving Integrator MB4 and Its Application to Quantum-Mechanical Wavepacket Dynamics
Tsubasa Sakai, Shuhei Kudo, Hiroto Imachi +3
In simulating physical systems, conservation of the total energy is often essential, especially when energy conversion between different forms of energy occurs frequently. Recently…
Fixed-point analysis of Ogita-Aishima's symmetric eigendecomposition refinement algorithm for multiple eigenvalues
Kurumi Shiroma, Yusaku Yamamoto
Recently, Ogita and Aishima proposed an efficient eigendecomposition refinement algorithm for the symmetric eigenproblem. Their basic algorithm involves division by the difference…
Shifted CholeskyQR for computing the QR factorization of ill-conditioned matrices
Takeshi Fukaya, Ramaseshan Kannan, Yuji Nakatsukasa +2
The Cholesky QR algorithm is an efficient communication-minimizing algorithm for computing the QR factorization of a tall-skinny matrix. Unfortunately it has the inherent numerical…
EigenKernel - A middleware for parallel generalized eigenvalue solvers to attain high scalability and usability
Kazuyuki Tanaka, Hiroto Imachi, Tomoya Fukumoto +5
An open-source middleware EigenKernel was developed for use with parallel generalized eigenvalue solvers or large-scale electronic state calculation to attain high scalability and…
Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product
Akira Imakura, Yusaku Yamamoto
The modified Gram-Schmidt (MGS) orthogonalization is one of the most well-used algorithms for computing the thin QR factorization. MGS can be straightforwardly extended to a non-st…
On the optimality and sharpness of Laguerre's lower bound on the smallest eigenvalue of a symmetric positive definite matrix
Yusaku Yamamoto
Lower bounds on the smallest eigenvalue of a symmetric positive definite matrices play an important role in condition number estimation and in iterativ…
A new subtraction-free formula for lower bounds of the minimal singular value of an upper bidiagonal matrix
Takumi Yamashita, Kinji Kimura, Yusaku Yamamoto
Traces of inverse powers of a positive definite symmetric tridiagonal matrix give lower bounds of the minimal singular value of an upper bidiagonal matrix. In a preceding work, a f…