Recent Publications:

  1. 1.X. Ye, W. Zhang, Y. Futamura and T. Sakurai, Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning, Cells, Vol.8, E1938 (15 pages) (2020).

  2. 2.X. Ye, W. Zhang and T. Sakurai, Adaptive Unsupervised Feature Learning for Gene Signature Identification in Non-Small-Cell Lung Cancer, IEEE Access, pp.154354-154362 (2020).

  3. 3.H. Li, X. Ye, A. Imakura and T. Sakurai, Hubness-based Sampling Method for Nystrom Spectral Clustering, Proc. the International Joint Conference on Neural Networks (IJCNN 2020), Glasgow (2020) (accepted).

  4. 4.J. Jair, C. Aranha and T. Sakurai, Proc. IEEE Congress on Evolutionary Computation (CEC), A Training Difficulty Schedule for Effective Search of Meta-Heuristic Design, Glasgow (2020) (accepted).

  5. 5.K. Sakamoto, K. Morikuni, T. Sakurai, S. Matsumoto and K. Vogt, Online spectral classification for long-term spike sorting, Proc. the third International Conference on Medical and Health Informatics 2020, Kamakura (2020) (accepted).

  6. 6.M. Matsuda, K. Morikuni, A. Imakura, X. Ye and T. Sakurai, Multiclass spectral feature scaling method for dimensionality reduction, Intelligent Data Analysis (2020) (accepted).

  7. 7.A. Imakura, X. Ye and T. Sakurai, Collaborative Data Analysis: Non-Model Sharing-Type Machine Learning for Distributed Data, Proceedings of the 2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), (2020) (accepted).

  8. 8.S. Baek, A. Imakura, T. Sakurai and I. Kataoka, Accelerating the Backpropagation algorithm by Using the NMF-based method on Deep Neural Networks, Proceedings of the 2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), (2020) (accepted).

  9. 9.H. Li, X. Ye, A. Imakura and T. Sakurai, Ensemble Learning for Spectral Clustering, Proceedings of the 20th IEEE International Conference on Data Mining (ICDM2020), Sorrento (2020) (accepted).

  10. 10.H. Yoda, A. Imakura, M. Matsuda, X. Ye and T. Sakurai, Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis, International Journal of Machine Learning and Computing (IJMLC), (2020) (accepted).

  11. 11.X. Ye, H. Li, A. Imakura and T. Sakurai, An oversampling framework for imbalanced classification based on Laplacian eigenmaps, Neurocomputing, Vol. 399, pp. 107-116 (2020).

  12. 12.A. Imakura, T. Sakurai, Data collaboration analysis framework using centralization of individual intermediate representations for distributed data sets, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., Vol. 6, 04020018 (2020).

  13. 13.Y. Futamura, X. Ye, A. Imakura and T. Sakurai, Spectral anomaly detection in large graphs using a complex moment-based eigenvalue solver, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 6, 04020010 (2020).

  14. 14.X. Ye, H. Li, T. Sakurai and P.W. Shueng, Ensemble feature learning to identify risk factors for predicting secondary cancer, International Journal of Medical Sciences, Vol. 16 (7), p. 949 (2019).

  15. 15.H. Chen, J. Meng, T. Sakurai and X. Wang, Backward error analysis for linearizations in heavily damped quadratic eigenvalue problem, Numerical Linear Algebra with Applications, Vol. 26, e2253 (2019).

  16. 16.L. Du, A. Imakura and T. Sakurai, Simultaneous band reduction of two symmetric matrices, Computers and Mathematics with Applications, 77 (8), 2pp.207-2220 (2019).

  17. 17.Y. Sekigawa, Y. Futamura, A. Imakura and T. Sakurai, Polynomial Preconditioner for Linear Systems with Multiple Right-Hand Sides and Multiple Shifts, Transactions of the Japan Society for Industrial and Applied Mathematics, Vol.29, pp. 41-164 (2019). (in Japanese)

  18. 18.T. Sakurai, Y. Futamura, A. Imakura and T. Imamura, Scalable Eigen-analysis engine for large-scale eigenvalue problems, Advanced Software Technologies for Post-Peta Scale Computing, pp.37-57 (2019).

  19. 19.X. Ye, H. Li, A. Imakura and T. Sakurai, Distributed Collaborative Feature Selection Based on Intermediate Representation, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, pp.4142–4149 (2019).

  20. 20.Y. C. Lavinas, C. Aranha and T. Sakurai, Using diversity as a priority function for resource allocation on MOEA/D. GECCO (Companion) pp.215-216 (2019).

  21. 21.A. Alvermann, A. Basermann, H.-J. Bungartz, C. Carbogno, D. Ernst, H. Fehske, Y. Futamura, M. Galgon, G. Hager, S. Huber, T. Huckle, A. Ida, A. Imakura, M. Kawai, S. Köcher, M. Kreutzer, P. Kus, B. Lang, H. Lederer, V. Manin, A. Marek, K. Nakajima, L. Nemec, K. Reuter, M. Rippl, M. Röhrig-Zöllner, T. Sakurai, M. Scheffler, C. Scheurer, F. Shahzad, D. S. Brambila, J. Thies and G. Wellein, Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects, Japan Journal of Industrial and Applied Mathematics, Vol.36, pp.699-717 (2019).

  22. 22.A. Imakura, M. Matsuda, X. Ye, T. Sakurai, Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction, the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, pp.3910-3918 (2019).

  23. 23.M. Matsuda, K. Morikuni and T. Sakurai, Spectral feature scaling method for supervised dimensionality reduction, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, pp.2560-2566 (2018).

  24. 24.H. Yamada, A. Imakura, T. Imamura, T. Sakurai, Optimization of Reordering Procedures in HOTRG for Distributed Parallel Computing, In proceeding of The 19th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2018), Vancouver, pp.957-966 (2018).

  25. 25.R. Arai, A. Imakura, T. Sakurai, An improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization for deep neural networks,  International Journal of Machine Learning and Computing (IJMLC), Vol.8, pp.191-197 (2018).

  26. 26.A. Imakura, Y. Futamura, and T. Sakurai, Structure-preserving of the block SS-Hankel method for solving, Hermitian generalized eigenvalue problems, Proc. PPAM 2017, Lecture Notes in Computer Science, Vol.10777, pp.600-611 (2018).

  27. 27.H. Suno, Y. Nakamura, K.-I. Ishikawa, Y. Kuramashi, Y. Futamura, A. Imakura, and T. Sakurai, Eigenspectrum Calculation of the O(a)-improved Wilson-Dirac Operator in Lattice QCD using the Sakurai-Sugiura Method, Proc. of International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA), Tsukuba, pp.81-90 (2018).

  28. 28.A. Imakura, Y. Inoue, Y. Futamura, T. Sakurai, Parallel implementation of the nonlinear semi-NMF based alternating optimization method for deep neural networks, Neural Processing Letters, Vol.47, pp.815–827 (2018).

  29. 29.X. Ye and T. Sakurai, Spectral Clustering with Adaptive Similarity Measure in Kernel Space, 22(4), International Journal of Intelligent Data Analysis, Vol.22, pp.751-765, (2018).

  30. 30.A. Imakura, T. Sakurai, Block SS-CAA: A complex moment-based parallel nonlinear eigensolver using the block communication-avoiding Arnoldi procedure, Parallel Computing, Vol.74, pp.34-48 (2018).

Books

  1. 1.Eigenvalue and Singular value computation, Lecture Course of Computational Mechanics, Maruzen, Tokyo (2019), ISBN9784621304730 (in Japanese).

  2. 2.Numerical Linear Algebra and HPC, Series of Industrial and Applied Mathematics, Kyoritsu Publishing, Tokyo (2018), ISBN978-4-320-01955-3 (in Japanese).

  3. 3.Numerical Computing and Analysis, The Society for the Promotion of the Open University of Japan, Tokyo (2014) ISBN-13 978-4595315046 (in Japanese).

  4. 4.Encyclopedia of Computational Mechanics (Japanese Edition), Asakura Pulishing, Tokyo (2010) ISBN978-4-254-23120-5 (in Japanese).

  5. 5.Encyclopedia of Mathematical Sciences, Maruzen, Tokyo (2009) ISBN978-4-621-08125-9 (in Japanese).

  6. 6.Art of Numerical Computation, Kyoritsu-Shuppan, Tokyo (2006) ISBN4-320-01803-6 (in Japanese).

  7. 7.Numerical Computing Tip, Kyoritsu-Shuppan, Tokyo (2004) ISBN4-320-12088-4 (in Japanese).

  8. 8.An Introduction to Numerical Methods with MATLAB and Scilab, University of Tokyo Press. Tokyo (2003) ISBN4-13-062450-4 (in Japanese).

  9. 9.Numerical Methods, Ohmsha Ltd. Tokyo (1998) ISBN4-274-13153-X (in Japanese).


Papers (Listed in subjects):

AI / Deep learning / Data analysis

  1. 1.X. Ye, W. Zhang, Y. Futamura and T. Sakurai, Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning, Cells, Vol.8, E1938 (15 pages) (2020).

  2. 2.X. Ye, W. Zhang and T. Sakurai, Adaptive Unsupervised Feature Learning for Gene Signature Identification in Non-Small-Cell Lung Cancer, IEEE Access, pp.154354-154362 (2020).

  3. 3.H. Li, X. Ye, A. Imakura and T. Sakurai, Hubness-based Sampling Method for Nystrom Spectral Clustering, Proc. the International Joint Conference on Neural Networks (IJCNN 2020), Glasgow (2020) (accepted).

  4. 4.J. Jair, C. Aranha and T. Sakurai, Proc. IEEE Congress on Evolutionary Computation (CEC), A Training Difficulty Schedule for Effective Search of Meta-Heuristic Design, Glasgow (2020) (accepted).

  5. 5.K. Sakamoto, K. Morikuni, T. Sakurai, S. Matsumoto and K. Vogt, Online spectral classification for long-term spike sorting, Proc. the third International Conference on Medical and Health Informatics 2020, Kamakura (2020) (accepted).

  6. 6.M. Matsuda, K. Morikuni, A. Imakura, X. Ye and T. Sakurai, Multiclass spectral feature scaling method for dimensionality reduction, Intelligent Data Analysis (2020) (accepted).

  7. 7.A. Imakura, X. Ye and T. Sakurai, Collaborative Data Analysis: Non-Model Sharing-Type Machine Learning for Distributed Data, Proceedings of the 2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), (2020) (accepted).

  8. 8.S. Baek, A. Imakura, T. Sakurai and I. Kataoka, Accelerating the Backpropagation algorithm by Using the NMF-based method on Deep Neural Networks, Proceedings of the 2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), (2020) (accepted).

  9. 9.H. Li, X. Ye, A. Imakura and T. Sakurai, Ensemble Learning for Spectral Clustering, Proceedings of the 20th IEEE International Conference on Data Mining (ICDM2020), Sorrento (2020) (accepted).

  10. 10.H. Yoda, A. Imakura, M. Matsuda, X. Ye and T. Sakurai, Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis, International Journal of Machine Learning and Computing (IJMLC), (2020) (accepted).

  11. 11.X. Ye, H. Li, A. Imakura and T. Sakurai, An oversampling framework for imbalanced classification based on Laplacian eigenmaps, Neurocomputing, Vol. 399, pp. 107-116 (2020).

  12. 12.A. Imakura, T. Sakurai, Data collaboration analysis framework using centralization of individual intermediate representations for distributed data sets, ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., Vol. 6, 04020018 (2020).

  13. 13.Y. Futamura, X. Ye, A. Imakura and T. Sakurai, Spectral anomaly detection in large graphs using a complex moment-based eigenvalue solver, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 6, 04020010 (2020).

  14. 14.X. Ye, H. Li, T. Sakurai and P.W. Shueng, Ensemble feature learning to identify risk factors for predicting secondary cancer, International Journal of Medical Sciences, Vol. 16 (7), p. 949 (2019).

  15. 15.L. Du, A. Imakura and T. Sakurai, Simultaneous band reduction of two symmetric matrices, Computers and Mathematics with Applications, 77 (8), 2pp.207-2220 (2019).

  16. 16.X. Ye, H. Li, A. Imakura and T. Sakurai, Distributed Collaborative Feature Selection Based on Intermediate Representation, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, pp.4142–4149 (2019).

  17. 17.Y. C. Lavinas, C. Aranha and T. Sakurai, Using diversity as a priority function for resource allocation on MOEA/D. GECCO (Companion) pp.215-216 (2019).

  18. 18.T. Sakurai, Y. Futamura, A. Imakura, International workshop on eigenvalue problems: algorithms; software and applications, in petascale computing (EPASA2018), Japan Journal of Industrial and Applied Mathematics, Vol.36, pp.561–562 (2019).

  19. 19.A. Imakura, M. Matsuda, X. Ye, T. Sakurai, Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction, the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, pp.3910-3918 (2019).

  20. 20.M. Matsuda, K. Morikuni and T. Sakurai, Spectral feature scaling method for supervised dimensionality reduction, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI2018), Stockholm, pp. 2560-2566 (2018).

  21. 21.A. Imakura, Y. Inoue, Y. Futamura, T. Sakurai, Parallel implementation of the nonlinear semi-NMF based alternating optimization method for deep neural networks, Neural Processing Letters, Vol.47, pp.815–827 (2018).

  22. 22.R. Arai, A. Imakura, T. Sakurai, An improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization for deep neural networks,  International Journal of Machine Learning and Computing (IJMLC), Vol.8, pp.191-197 (2018).

  23. 23.X. Ye and T. Sakurai, Spectral Clustering with Adaptive Similarity Measure in Kernel Space, 22(4), International Journal of Intelligent Data Analysis, Vol.22, pp.751-765, (2018).

  24. 24.X. Ye and T. Sakurai, Similarity measure based on adaptive neighbors for spectral clustering, 9th International Conference on Machine Learning and Computing (ICMLC 2017), Singapore, pp.405-409 (2017).

  25. 25.T. Sakurai, A. Imakura, Y. Inoue and Y. Futamura, Alternating optimization method based on nonnegative matrix factorizations for deep neural networks, In Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP 2016), Kyoto, LNCS 9950, pp. 354-362 (2016).

  26. 26.X. Ye, K. Ji and T. Sakurai, Global discriminant analysis for unsupervised feature selection with local structure preservation, Proceeding of the Florida Artificial Intelligence Research Society Conference (FLAIRS-29), pp. 454-459 (2016).

  27. 27.X. Ye and T. Sakurai, Robust similarity measure for spectral clustering based on shared neighbors, to be published in ETRI Journal (2016).

  28. 28.X. Ye, K. Ji and T. Sakurai, Unsupervised feature selection with correlation and individuality analysis, International Journal of Machine Learning and Computing (IJMLC), Vol.6, No.1, pp.36-41 (2016). 

  29. 29.X. Ye, K. Ji and T. Sakurai, Spectral clustering and discriminant analysis for unsupervised feature selection, Proceeding of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), pp. 563-568 (2016).

  30. 30.X. Ye and T. Sakurai, Spectral Clustering using robust similarity measure based on closeness of shared nearest neighbors, Proceeding of the International Joint Conference on Neural Networks (IJCNN), pp. 1-8 (2015).


Eigenvalue problems / Parallel computing / Applications

  1. 1.H. Chen, J. Meng, T. Sakurai and X. Wang, Backward error analysis for linearizations in heavily damped quadratic eigenvalue problem, Numerical Linear Algebra with Applications, Vol. 26, e2253 (2019).

  2. 2.A. Alvermann, A. Basermann, H.-J. Bungartz, C. Carbogno, D. Ernst, H. Fehske, Y. Futamura, M. Galgon, G. Hager, S. Huber, T. Huckle, A. Ida, A. Imakura, M. Kawai, S. Köcher, M. Kreutzer, P. Kus, B. Lang, H. Lederer, V. Manin, A. Marek, K. Nakajima, L. Nemec, K. Reuter, M. Rippl, M. Röhrig-Zöllner, T. Sakurai, M. Scheffler, C. Scheurer, F. Shahzad, D. S. Brambila, J. Thies and G. Wellein, Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects, Japan Journal of Industrial and Applied Mathematics, Vol.36, pp.699-717 (2019).

  3. 3.T. Sakurai, Y. Futamura, A. Imakura and T. Imamura, Scalable Eigen-analysis engine for large-scale eigenvalue problems, Advanced Software Technologies for Post-Peta Scale Computing, pp.37-57 (2019).

  4. 4.A. Imakura, Y. Futamura, and T. Sakurai, Structure-preserving of the block SS-Hankel method for solving, Hermitian generalized eigenvalue problems, Proc. PPAM 2017, Lecture Notes in Computer Science, Vol.10777, pp.600-611 (2018).

  5. 5.A. Imakura, T. Sakurai, Block SS-CAA: A complex moment-based parallel nonlinear eigensolver using the block communication-avoiding Arnoldi procedure, Parallel Computing, 74, 34-48 (2018).

  6. 6.S. Iwase, Y. Futamura, A. Imakura, T. Sakurai, T. Ono, Efficient and Scalable Calculation of Complex Band Structure using Sakurai-Sugiura Method, The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17), 402-403-404 (2017).

  7. 7.A. Nakata, Y. Futamura, T. Sakurai, D.R. Bowler and T. Miyazaki, Efficient calculation of electronic structure using O(N) density functional theory, J. Chem. Theory Comput., 13 (9), pp 4146–4153 (2017).

  8. 8.H. Chen, A. Imakura, T. Sakurai, Improving backward stability of Sakurai-Sugiura method with balancing technique in polynomial eigenvalue problem, Applications of Mathematics, 62 (4), 357-375 (2017).

  9. 9.Y. Futamura, T. Yano, A. Imakura, T. Sakurai, A real-valued block conjugate gradient type method for solving complex symmetric linear systems with multiple right-hand sides, Applications of Mathematics, 62 (4), 333-355 (2017).

  10. 10.A. Imakura, Y. Futamura and T. Sakurai, An error resilience strategy of a complex moment-based eigensolver, Proc. of International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA), Tsukuba, Lecture Notes in Computational Science and Engineering, Vol.117, pp.1–18 (2017).

  11. 11.H. Suno, Y. Nakamura, K.-I. Ishikawa, Y. Kuramashi, Y. Futamura, A. Imakura, and T. Sakurai, Eigenspectrum Calculation of the O(a)-improved Wilson-Dirac Operator in Lattice QCD using the Sakurai-Sugiura Method, Proc. of International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA), Tsukuba, pp.81-90 (2017).

  12. 12.H. Chen, Y. Maeda, A. Imakura, T. Sakurai, F. Tisseur, Improving the numerical stability of the Sakurai-Sugiura method for quadratic eigenvalue problems, JSIAM Letters, Vol. 9, pp. 17-20 (2017).

  13. 13.J. Xiao, C. Zhang, TM. Huang and T. Sakurai, Solving large-scale nonlinear eigenvalue problems by rational interpolation and resolvent sampling based Rayleigh-Ritz method, Int. J. Numer. Methods Eng., Vol. 110, No. 2, pp. 776-800 (2017).

  14. 14.A. Imakura and T. Sakurai, Block Krylov-type complex moment-based eigensolvers for solving generalized eigenvalue problems, Numer. Alg., Vol. 75, No. 2, pp. 413-433 (2017).

  15. 15.Y. Nagai, Y. Shinohara, Y. Futamura and T. Sakurai, Reduced-shifted conjugate-gradient method for a Green's function: Efficient numerical approach in a nano-structured superconductor, J. Phys. Soc. Japan, Vol. 86, 014708 (9 pages) (2017).

  16. 16.A. Imakura, L. Du, T. Sakurai, Relationships among contour integral-based methods for solving generalized eigenvalue problems, Japan Journal of Industrial and Applied Mathematics, Vol. 33, No. 3, pp. 721-750 (2016).

  17. 17.T. Ide, Y. Inoue, Y. Futamura, T. Sakurai, Highly parallel computation of generalized eigenvalue problem in vibration for automatic transmission of vehicles using the Sakurai–Sugiura method and supercomputers, Mathematical Analysis of Continuum Mechanics and Industrial Applications,  Vol. 26, pp. 207-218 (2016).

  18. 18.T. Ide, K. Toda, Y. Futamura and T. Sakurai, Highly Parallel Computation of Eigenvalue Analysis in Vibration for Automatic Transmission using Sakurai-Sugiura Method and K-Computer, SAE Technical Papers, 2016-01-1378 (2016) (doi:10.4271/2016-01-1378).

  19. 19.N. Shimizu, Y. Utsuno, Y. Futamura and T. Sakurai, Stochastic estimation of nuclear level density in the nuclear shell model: an application to parity-dependent level density in $^{58}$ Ni, Phys. Lett. B 753, 13-17 (2016) (DOI: 10.14495/jsiaml.7.53).

  20. 20.T. Hasegawa, A. Imakura, and T. Sakurai, Recovering from accuracy deterioration in the contour integral-based eigensolver, JSIAM Letters, Vol. 8, pp. 1-4 (2016).

  21. 21.A. Imakura, L. Du and T. Sakurai, Error bounds of Rayleigh–Ritz type contour integral-based eigensolver for solving generalized eigenvalue problems, Numerical Algorithms, Vol.71, pp 103-120 (2016).

  22. 22.Y. Maeda, Y. Futamura, A. Imakura and T. Sakurai, Filter analysis for the stochastic estimation of eigenvalue counts, JSIAM Letters, Vol. 7, pp. 53-56 (2015).

  23. 23.Y. Maeda and T. Sakurai, A method for eigenvalue problem in arcuate region using contour integral, IPSJ Trans. ACS, Vol. 8, No. 4, pp. 88-97 (2015).

  24. 24.A. Imakura, L. Du and T. Sakurai, A map of contour integral-based eigensolvers for solving generalized eigenvalue problems, arXiv preprint arXiv:1510.02572 (2015).

  25. 25.Y. Futamura, S. Hashimoto, A. Imakura, K. Nagata, T. Sakurai, A filtering technique for the temporally reduced matrix of the Wilson fermion determinant, Proc. The 32nd International Symposium on Lattice Field Theory, New York, (2014). (on line)

  26. 26.A. Imakura, L. Du, T. Sakurai, A block Arnoldi-type contour integral spectral projection method for solving generalized eigenvalue problems, Appl. Math. Lett., Vol. 32, pp. 22-27 (2014).

  27. 27.I. Yamazaki, T. Ikegami, H. Tadano, T. Sakurai, Performance comparison of parallel eigensolvers based on a contour integral method and a Lanczos method, Parallel Computing, Vol. 39, pp. 280-290 (2013).

  28. 28.Y. Futamura, T. Sakurai, S. Furuya, J.-I. Iwata, Efficient Algorithm for Linear Systems Arising in Solutions of Eigenproblems and its Application to Electronic-Structure Calculations, Proc. 10th International Meeting on High-Performance Computing for Computational Science (VECPAR 2012), pp.226-235 (2013).

  29. 29.T. Yano, Y. Futamura, T. Sakurai, Multi-GPU scalable implementation of a contour-integral-based eigensolver for real symmetric dense generalized eigenvalue problems, Proc. 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2013), pp. 121-127 (2013).

  30. 30.G. Antoniu, T. Boku, A. Buttari, C. Calvin, P. Codognet, M. Daydé, N. Emad, Y. Ishikawa, G. Joslin, S. Matsuoka, K. Nakajima, H. Nakashima, R. Namyst, S. Petiton, T. Sakurai, M. Sato, Towards exascale with the ANR-JST japanese-french project FP3C, Computer Science and Information Technologies (CSIT) 2013, pp. 1-10 (2013).

  31. 31.Y. Nagai, Y. Shinohara, Y. Futamura, Y. Ota, T. Sakurai, Numerical construction of a low-energy effective Hamiltonian in a self-consistent Bogoliubov-de Gennes approach of superconductivity, J. Phys. Soc. Jpn., Vol. 82, (094701) (2013).

  32. 32.T. Sakurai, Y. Futamura, H. Tadano, Efficient parameter estimation and implementation of a contour integral-based eigensolver, J. Algo. Comput. Tech., Vol. 7, pp. 249-269, (2013).

  33. 33.S. Yokota, T. Sakurai, A projection method for nonlinear eigenvalue problems using contour integrals, JSIAM Letters, Vol. 5, pp. 41-44 (2013).

  34. 34.T. Mizusaki, K. Kaneko, M. Honma, T. Sakurai, Filter diagonalization: A new method for large-scale shell-model calculations, Acta Phys. Polonica B, Vol. 42, PP. 447-450 (2011).

  35. 35.Y. Maeda, Y. Futamura, T. Sakurai, Stochastic estimation method of eigenvalue density for nonlinear eigenvalue problem on the complex plane, JSIAM Letters, Vol. 3, pp. 61-64 (2011).

  36. 36.T. Ikegami, T. Sakurai, H. Tadano, Parallel eigensolver for large scale non‐linear systems, Proc. ICNAAM 2010, pp. 941-944 (2010).

  37. 37.K. Senzaki, H. Tadano, T. Sakurai, Z. Bai, A method for profiling the distribution of eigenvalues using the AS method, Taiwanese J. Math., Vol. 14, pp. 839-853 (2010).

  38. 38.T. Ikegami, T. Sakurai, U. Nagashima, Contour integral eigensolver for non-Hermitian systems: a Rayleigh-Ritz-type approach, Taiwanese J. Math., Vol. 14, pp. 825-837 (2010).

  39. 39.T. Sakurai, H. Tadano, T. Ikegami, U. Nagashima, A parallel eigensolver using contour integration for generalized eigenvalue problems in molecular simulation, Taiwanese J. Math., Vol. 14, pp. 855-867 (2010).

  40. 40.Y. Futamura, H. Tadano, T. Sakurai, Parallel stochastic estimation method of eigenvalue distribution, JSIAM Letters, Vol. 2, pp. 127-130 (2010).

  41. 41.T. Mizusaki, K. Kaneko, M. Honma, and T. Sakurai, Filter diagonalization of shell-model calculations, Phys. Rev. C 82, 024310 (2010). [10 pages]

  42. 42.H. Ohno, Y. Kuramashi, H. Tadano, T. Sakurai, A quadrature-based eigensolver with a Krylov subspace method for shifted linear systems for Hermitian eigenproblems in lattice QCD, JSIAM Letters, Vol. 2, pp. 115-118 (2010).

  43. 43.H. Umeda, Y. Inadomi, T. Watanabe, T. Uagi, T. Ishimoto, T. Ikegami, H. Tadano, T. Sakurai and U. Nagashima, Parallel Fock matrix construction with distributed shared memory model for the FMO-MO method, J. Comput. Chem., Vol. 31, pp. 2381-2388 (2010).

  44. 44.J. Asakura, T. Sakurai, H. Tadano, T. Ikegami and K. Kimura, A numerical method for polynomial eigenvalue problems using contour integral, Japan J. Indust. Appl. Math., Vol. 27, pp. 73-90 (2010).

  45. 45.T. Ikegami, T. Sakurai and U. Nagashima, A filter diagonalization for generalized eigenvalue problems based on the Sakurai-Sugiura projection method, J. Comput, Appl. Math., Vol. 33, 1927-1936 (2010).

  46. 46.T. Sakurai, J. Asakura, H. Tadano and T. Ikegami, Error analysis for a matrix pencil of Hankel matrices with perturbed complex moments, JSIAM Letters, Vol. 1, pp. 76-79 (2009).

  47. 47.J. Asakura, T. Sakurai, H. Tadano, T. Ikegami and K. Kimura, A numerical method for nonlinear eigenvalue problems using contour integrals, JSIAM Letters, Vol.1, pp. 52-55 (2009).

  48. 48.T. Kihara, H. Tadano, T. Sakurai, Implementation and performance evaluation of sparse matrix vector multiplication for mixed precision Krylov method on the Cell BE, IPSJ Transactions on Advanced Computing Systems, Vol. 1, pp. 51-60 (2008) (in Japanese). 

  49. 49.T. Sakurai, Y. Kodaki, H. Tadano, D. Takahashi, M. Sato and U. Nagashima, A parallel method for large sparse generalized eigenvalue problems using a grid RPC system, Special Issue of Future Generation Computer Systems on Applications of Distributed and Grid Computing, Vol. 24, pp. 613-619 (2008).

  50. 50.T. Watanabe, Y. Inadomi, T. Ishimoto, H. Umeda, T. Sakurai and U. Nagashima, Molecular orbital calculation for large molecule, J. Comput. Chem. Japan, Vol. 6, No. 3, pp. 217-226 (2007).

  51. 51.K. Senzaki, H. Tadano and T. Sakurai, A method for estimating a distribution of eigenvalues using the AMLS method, Trans. Japan SIAM, Vol. 17, pp. 511-521 (2007) (in Japanese).

  52. 52.T. Sakurai, H. Tadano, CIRR: a Rayleigh-Ritz type method with contour integral for generalized eigenvalue problems, Proc. The First China-Japan-Korea Joint Conference on Numerical Mathematics, Special Issue of Hokkaido Mathematical Journal, Vol. 36,  pp. 745-757 (2007).

  53. 53.Y. Aida, Y. Nakajima, M. Sato, O. Tatebe and T. Sakurai, Performance improvement by distributed data management layer on Grid RPC system, IPSJ Transactions on Advanced Computing Systems Vol. 48, No. SIG8, pp. 127-144 (2007) (in Japanese).

  54. 54.T. Sakurai, Y. Kodaki, H. Tadano, H. Umeda, Y. Inadomi, T. Watanabe and U. Nagashima, A master-worker type eigensolver for molecular orbital computations, Proc. Applied Parallel Computing. State of the Art in Scientific Computing, Lecture Notes in Computer Science, No. 4699, pp. 617-625 (2007).

  55. 55.T. Kihara, Y. Kodaki, H. Tadano and T.sakurai, Modified multiple explicitly restarted Arnoldi method with hybrid GridRPC/MPI implementation, IPSJ Transactions on Advanced Computing Systems, Vol. 48, No. SIG8, pp. 94-103 (2007) (in Japanese).

  56. 56.T. Watanabe, Y. Inadomi, T. Ishimoto, H. Umeda, T. Sakurai and U. Nagashima, Molecular Orbital Calculation for Large Molecule, J. Comput. Chem. Japan, Vol. 6, No. 3, pp 217-226 (2007).

  57. 57.Y. Aida, Y. Nakajima, M. Sato, T. Sakurai, D. Takahashi and T. Boku, Performance improvement by data management layer in a Grid RPC system, Lecture Notes in Computer Science, No. 3947, pp. 324-335 (2006).

  58. 58.T. Sakurai, Y. Kodaki, H. Umeda, Y. Inadomi, T. Watanabe and U. Nagashima, A hybrid parallel method for large sparse eigenvalue problems on a grid computing environment using Ninf-G/MPI, Proc. 5th International Conference on Large-Scale Scientific Computations, LSSC'05, Sozopol, Lecture Notes in Computer Science, No. 3743, pp. 438-445 (2006).

  59. 59.Y. Inadomi, H. Umeda, T. Watanabe, T. Sakurai and U. Nagashima, Large-scale molecular orbital calculation using FMO-MO method, Trans. Japan SIAM, Vol. 15, No. 2, pp. 125-135 (2005) (in Japanese).

  60. 60.Y. Inadomi, H. Umeda, T. Watanabe, T. Sakurai and U. Nagashima, Large-scale Molecular Orbital Calculation by FMO-MO Method: for Grid Computing, IPSJ Transactions on Advanced Computing Systems, Vol. 46, No. 10, pp. 44-51 (2005) (in Japanese).

  61. 61.T. Sakurai, K. Hayakawa, M. Sato and D. Takahashi, A master-worker type parallel method for large-scale eigenvalue problems, IPSJ Transactions on Advanced Computing Systems, Vol. 46, No. 10, pp. 1-8 (2005) (in Japanese).

  62. 62.T. Sakurai, K. Hayakawa, M. Sato and D. Takahashi, A parallel method for large sparse generalized eigenvalue problems by OmniRPC in a grid environment, Proc. Workshop on State-of-the-Art in Scientific Computing PARA'04, Lyngby, Lecture Notes in Computer Science, No. 3732, pp. 1151-1158 (2005).

  63. 63.T. Sakurai, H. Tadano, Y. Inadomi and U. Nagashima, A moment-based method for large-scale generalized eigenvalue problems, Appl. Num. Anal. Comp. Math. Vol. 1, No. 3, pp. 516-523 (2004).

  64. 64.T. Sakurai, H. Tadano, Y. Inadomi and U. Nagashima, A moment-based method for large scale eigenvalue problems, Proc. ICNAAM, Chalkis, pp. 333-336 (2004).

  65. 65.M. Ogasawara, H. Tadano, T. Sakurai and S. Itoh, A Krylov subspace method for shifted linear systems and its application to eigenvalue problems, Trans. Japan SIAM, Vol. 14, No. 3 (2004) (in Japanese).

  66. 66.T. Sakurai, A parallel method for large scale generalized eigenvalue problems, Bulletin of the Japan SIAM, Vol. 13, No. 4, pp. 308-317 (2003) (in Japanese) .

  67. 67.T. Sakurai and H. Sugiura, A projection method for generalized eigenvalue problem using numerical integration s, J. Comput. Appl. Math. Vol. 159, pp. 119-128 (2003).


Tensor computation / Linear solver / Krylov subspace / Preconditioner / QCD Simulation

  1. 1.Y. Sekigawa, Y. Futamura, A. Imakura and T. Sakurai, Polynomial Preconditioner for Linear Systems with Multiple Right-Hand Sides and Multiple Shifts, Transactions of the Japan Society for Industrial and Applied Mathematics, Vol.29, pp. 41-164 (2019).

  2. 2.H. Yamada, A. Imakura, T. Imamura, T. Sakurai, Optimization of Reordering Procedures in HOTRG for Distributed Parallel Computing, In proceeding of The 19th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2018), Vancouver, pp.957-966 (2018).

  3. 3.L. Su, A. Imakura, H. Tadano, T. Sakurai, Improving the convergence behaviour of BiCGSTAB by applying D-norm minimization, JSIAM Letters, Vol. 7, pp. 37-40 (2015).

  4. 4.L. Du, Y. Futamura, T. Sakurai, Block conjugate gradient type methods for the approximation of bilinear form CHA-1B, Comput. Math. Appl., Vol. 66, pp. 2446-2455 (2014).

  5. 5.A. Imakura, T. Sakurai, K. Sumiyoshi, H. Matsufuru, An auto-tuning technique of the weighted Jacobi-type iteration used for preconditioners of Krylov subspace methods, Proc. 2012 IEEE 6th International Symposium on Embedded Multicore Socs (MCSoC), pp. 183-190 (2012).

  6. 6.A. Imakura, T. Sakurai, K. Sumiyoshi, H. Matsufuru, A parameter optimization technique for a weighted Jacobi-type preconditioner, JSIAM Letters, Vol. 4, pp. 41-44 (2012).

  7. 7.M. Naito, H. Tadano, T. Sakurai, A modified Block IDR(s) method for computing high accuracy solutions, JSIAM Letters, Vol. 4, pp. 25-28 (2012).

  8. 8.I. Yamazaki, H. Tadano, T. Sakurai, K. Teranishi, A convergence improvement of the BSAIC preconditioner by deflation, JSIAM Letters, Vol. 3, pp. 5-8 (2011).

  9. 9.Y. Nakamura, K.-I. Ishikawa, Y. Kuramashi, T. Sakurai, H. Tadano, Modified block BiCGSTAB for lattice QCD, Comput. Phys. Commun., Vol. 183, pp. 34-37 (2011).

  10. 10.I. Yamazaki, M. Okada, H. Tadano, T. Sakurai, K. Teranishi, A block sparse approximate inverse with cutoff preconditioner for semi-sparse linear systems derived from Molecular Orbital calculations, JSIAM Letters, Vol. 2, pp. 41-44 (2010).

  11. 11.H. Tadano, Y. Kuramashi and T. Sakurai, Application of preconditioned block BiCGGR to the Wilson-Dirac equation with multiple right-hand sides in lattice QCD, Comput. Phys. Commun., Vol. 181, pp. 883-886 (2010).

  12. 12.T. Sakurai, H. Tadano and Y. Kuramashi, Application of block Krylov subspace algorithms to  the Wilson-Dirac equation with multiple right-hand sides in lattice QCD, Comput. Phys. Commun., Vol. 181, pp. 113-117 (2010).

  13. 13.H. Tadano, T. Sakurai and Y. Kuramashi, Block BiCGGR: A new block Krylov subspace method for computing high accuracy solutions, JSIAM Letters, Vol.1, pp. 44-47 (2009).

  14. 14.H. Tadano and T. Sakurai, A block Krylov subspace method for the contour integral method and its application to molecular orbital computations, IPSJ Transactions on Advanced Computing Systems, Vol. 2 No. 2, pp. 10-18 (2009) (in Japanese).

  15. 15.I. Yamazaki, M. Okada, H. Tadano, T. Sakurai and K. Teranishi, A performance evaluation of the preconditioning using double Cutoff, Trans. Japan SIAM, Vol. 18, No. 4, pp. 631-651 (2008) (in Japanese).

  16. 16.H. Tadano and T. Sakurai, On single precision preconditioners for Krylov subspace iterative methods, Lecture Notes in Computer Science, No. 4818, pp. 708-715 (2007).

  17. 17.M. Okada, T. Sakurai and K. Teranishi, A preconditioning using sparse direct solvers for approximate coefficient matrices, Trans. Japan SIAM, Vol. 17, No. 3, pp. 319-329 (2007) (in Japanese).

  18. 18.M. Okada, H. Tadano and T. Sakurai, On an evaluation method of preconditioners for complex symmetric systems of linear equations, Trans. Japan SIAM, Vol. 16, No. 4, pp. 497--505 (2006) (in Japanese).

  19. 19.H. Tadano and T. Sakurai, A stabilization of the CGS method by avoiding near-breakdown, Proc. ICNAAM2005, Rhodos, pp. 510-513 (2005).

  20. 20.H. Tadano and T. Sakurai, A method for avoiding breakdown in product-type iterative methods and its behavior for Toeplitz linear systems, Appl. Num. Anal. Comp. Math. Vol. 2, No. 2, pp. 245--261 (2005).

  21. 21.H. Tadano and T. Sakurai, A stabilization of the CGS method by restarting Lanczos process, Trans. Japan SIAM, Vol. 15, No. 2, pp. 85--99 (2005) (in Japanese).

  22. 22.H. Tadano and T. Sakurai, A method for avoiding breakdown in product-type iterative methods and its behavior for Toeplitz linear systems, Proc. ICNAAM, Chalkis, pp. 384--387 (2004).

  23. 23.T. Sakurai, K. Takabayashi and M. Natori, A method to avoid numerical instability of a recurrence relation for Lanczos polynomial, Tran. Inform. Process. Soc. Japan, Vol. 40, No. 12, pp. 4169--4177 (1999) (in Japanese).

  24. 24. C.H. Jin, S.L. Zhang, M. Natori, T. Sakurai and W.D. Zhou, Modified CGS method for the iterative solution of nonsymmetric linear systems, Trans. Inform. Process. Soc. Japan, Vol. 37, No. 11, pp. 2138--2141 (1996) (in Japanese).


Nonlinear equations / Root finding / Factoring method / Interval analysis / Rational approximation

  1. 1.T. Sakurai, J. Asakura, H. Tadano, T. Ikegami and K. Kimura, A method for finding zeros of polynomial equations using a contour integral based eigensolver, Proc. Symbolic Numeric Computations 2009, Kyoto, pp. 143-147 (2009).

  2. 2.X. Niu, T. Sakurai and H. Sugiura, A verified method for bounding clusters of zeros of analytic functions, J. Comput. Appl. Math., Vol. 199, No. 2, pp. 263--270 (2007).

  3. 3.X. Niu, T. Sakurai and H. Sugiura, A verified method for finding polynomial factors of analytic functions, Trans. Japan SIAM, Vol. 14, No. 3, pp. 193--205 (2004) (in Japanese).

  4. 4.M. S. Petkovic, T. Sakurai and L. Rancic, Family of simultaneous methods of Hansen-Patrick's type, Appl. Numer. Math. Vol 50, No. 3-4, pp. 489--510 (2004).

  5. 5.X. Niu and T. Sakurai, An eienvalue metod for finding the muliple zeros of a polynomial, Trans. Japan SIAM, Vol. 13, No. 4, pp. 447--460 (2003) (in Japanese).

  6. 6.P. Kravanja, T. Sakurai, H. Sugiura and M. Van Barel, A perturbation result for generalized eigenvalue problems and its application to error estimation in a quadrature method for computing zeros of analytic functions, J. Comput. Appl. Math. Vol. 161, No. 2, pp. 339--347 (2003).

  7. 7.P. Kravanja, T. Sakurai and M. Van Barel, Error analysis of a derivative-free algorithm for computing zeros of holomorphic functions, Computing, Vol. 70, pp. 335--347 (2003) .

  8. 8.X. Niu and T. Sakurai, A method for finding the zeros of polynomials using a companion matrix, Japan J. Indust. Appl. Math. Vol. 20, pp. 239--256 (2003).

  9. 9.T. Sakurai, P. Kravanja, H. Sugiura and M. Van Barel, An error analysis of two related quadrature methods for computing zeros of analytic functions, J. Comput. Appl. Math. Vol. 152, pp. 467--80 (2003).

  10. 10.T. Sakurai and H. Sugiura, On convergence behavior of an iterative method for finding polynomial factors of analytic functions, Proc. Fifth China-Japan Seminar on Numerical Mathematics, Shanghai, pp. 75--83 (2002),

  11. 11.T. Sakurai and H. Sugiura, Improvement of convergence of an iterative method for finding polynomial factors of analytic functions, J. Comput. Appl. Math. Vol. 140, No. 1--2, pp. 713--725 (2002).

  12. 12.T. Sakurai and H. Sugiura, On factorization of analytic functions and its verification, Reliable Computing, Vol. 6, No. 4, pp. 459--470 (2000).

  13. 13.P. Kravanja, T. Sakurai and M. Van Barel, On locating clusters of zeros of analytic functions, BIT, Vol. 39, No. 4, pp. 646--682 (1999).

  14. 14.T. Torii, N. Ohsako, H. Sugiura and T. Sakurai, A stably modified Euclidean algorithm for complex coefficient polynomials, Proc. ICIAM'95, Hamburg, pp. 565--566 (1996).

  15. 15.T. Sakurai, T. Torii, N. Ohsako and H. Sugiura, A method for finding clusters of zeros of analytic function, Proc. ICIAM'95, Hamburg, pp. 515--516 (1996).

  16. 16.T. Sakurai and M. S. Petkovic, On some simultaneous methods based on Weierstrass' correction, J. Comput. Appl. Math. Vol. 72, pp. 275--291 (1996).

  17. 17.C. Carstensen and T. Sakurai, Simultaneous factorization of a polynomial by rational approximation, J. Comput. Appl. Math. Vol. 61, No. 2, pp. 165--178 (1995).

  18. 18.N. Ohsako, T. Torii, H. Sugiura and T. Sakurai, A stable method for generating complex coefficient polynomial remainder sequence, Trans. Japan SIAM, Vol. 5, No. 3, pp. 241--255 (1995) (in Japanese).

  19. 19.T. Sakurai, H. Sugiura and T. Torii, A multistep iterative method for a nonlinear equation by using Durand-Kerner type auxiliary function, Trans. Japan SIAM, Vol. 4, No. 2, pp. 67--80 (1994) (in Japanese).

  20. 20.T. Torii, T. Sakurai and H. Sugiura, An application of Sunzi's theorem for solving algebraic equations, Proc. 1st China-Japan Seminar on Numerical Mathematics, pp. 155--167 (1993).

  21. 21.T. Torii and T. Sakurai, Global method for the poles of analytic function by rational interpolant on the unit circle, in Contributions in Numerical Mathematics, pp. 389--398, R. P. Agarwal ed. World Scientific Series in Applicable Analysis, World Scientific, Singapore, 1993.

  22. 22.T. Sakurai, H. Sugiura and T. Torii, Numerical factorization of a polynomial by rational Hermite interpolation, Numer. Algorithms, Vol. 3, No. 3, pp. 411--418 (1992).

  23. 23.S. Sonoda, T. Sakurai, H. Sugiura and T. Torii, A factoring method for a polynomial by divide and conquer method, Trans. Japan SIAM, Vol. 1, No. 4, pp. 277--290 (1991) (in Japanese).

  24. 24.T. Sakurai, T. Torii and H. Sugiura, A high order iterative formula for simultaneous determination of zeros of a polynomial, J. Comput. Appl. Math. Vol. 38, No. 23, pp. 387--397 (1991).

  25. 25.T. Sakurai, T. Torii and H. Sugiura, An iterative method for algebraic equation by Pade approximation, Computing, Vol. 46, No. 2, pp. 131--141 (1991).

  26. 26.T. Sakurai, T. Torii and H. Sugiura, A class of simultaneous iterative formulae with high convergence order, Tran. Inform. Process. Soc. Japan, Vol. 31, No. 7, pp. 964--969 (1990) (in Japanese).

  27. 27.T. Sakurai, T. Torii and H. Sugiura, An iterative method for algebraic equations based on Pade approximation, Tran. Inform. Process. Soc. Japan, Vol. 31, No. 4, pp. 517--522 (1990) (in Japanese).

  28. 28.T. Sakurai, T. Torii and H. Sugiura, An iterative method for algebraic equations based on electrostatic interpretation, Tran. Inform. Process. Soc. Japan, Vol. 29, No. 5, pp. 447--455 (1988) (in Japanese).


Numerical conformal mappings / Pade approximation

  1. 1.Y. Lu, T. Itoh, T. Sakurai, A numerical method for the conformal mapping of multiply-connected domains using Pade approximation, Trans. Jpan SIAM, Vol. 15, No. 3, pp. 149--164 (2006) (in Japanese).

  2. 2.Y. Lu, T. Itoh, S. Itoh, T. Sakurai, Improving the accuracy of numerical conformal mapping by Pade approximation using the Arnoldi method, J. Inform. Comput. Sci. Vol. 2 No. 2, pp. 289--294 (2005).

  3. 3.Y. Lu, T. Itoh, S. Itoh and T. Sakurai, The accuracy improvement of numerical conformal mapping using Pade approximation by Arnoldi method, Trans. Japan SIAM, Vol. 15, No. 3, pp. 495-300 (2005), Y. Inadomi, H. Umeda, T. Watanabe, T. Sakurai and U. Nagashima, Large-scale molecular orbital calculation using FMO-MO method, Trans. Japan SIAM, Vol. 15, No. 2, pp. 125--135 (2005) (in Japanese).

  4. 4.T. Sakurai and H. Sugiura, A method for numerical conformal mappings by using Pade approximations, Tran. Inform. Process. Soc. Japan, Vol. 43, No. 9, pp. 2959--2962 (2002) (in Japanese).

  5. 5.E. Song, H. Sugiura and T. Sakurai, On analysis of instability of Wegmann's method for numerical conformal mapping and its stabilizing, Tran. Inform. Process. Soc. Japan, Vol. 32, No. 2, pp. 126--132 (1991) (in Japanese).

  6. 6.E. Song, H. Sugiura and T. Sakurai, On a solution of Teodorsen's equation for numerical conformal mapping, Tran. Inform. Process. Soc. Japan, Vol. 30, No. 4, pp. 393--401 (1989) (in Japanese).


Inverse problems / Complex moments / EEG

  1. 1. N. Ito, T. Nara and T. Sakurai, Image feature extraction based on complex moments, Tran. Japan SIAM, Vol. 18, No. 1, pp. 135-153 (2008) (in Japanese).

  2. 2.J. Oohama, T. Sakurai and T. Nara, An error analysis of a direct reconstruction method for the EEG inverse source problem, Trans. Japan SIAM, Vol. 15, No. 3, pp. 483-494 (2005) (in Japanese).


Numerical Integration / Quadrature formula

  1. 1.H. Sugiura and T. Sakurai, On the construction of high-order integration formulae for the adaptive quadrature method, J. Comput. Appl. Math. Vol. 28, No. 3, pp. 367--381 (1989).


Data retrieval / MathML / Vector space method / Front-end Tool / GUI / Mathematical computing environment

  1. 1.S. Kishimoto, M. Murakata, T. Nakanishi, T. Sakurai, T. Kitagawa, Problem solving support system for mathematic, Proc. DEWS2007, CD-ROM (2007) (in Japanese).

  2. 2.M. Murakata, T. Otsuka, S. Kishimoto, T. Nakanishi, T. Sakurai, T. Kitagawa, Realization of the input support GUI "MathGUIde" for a complex mathematical formulas search, Proc. DEWS2006, CD-ROM (2006) (in Japanese).

  3. 3.S. Kishimoto, T. Nakanishi, M. Murakata, T. Otsuka, T. Sakurai, T. Kitagawa, An implementation method of an integrated associative search for mathematical expressions, The IASTED International Conference on Databases and Applications, DBA 2006, Innsbruck, pp.160-167 (2006).

  4. 4.T. Nakanishi, S. Kishimoto, M. Murakata, T. Otsuka, T. Sakurai and T. Kitagawa, An implementation method of composite association retrieval system for data of mathematical formulas, DBSJ Letters. Vol. 4, No. 1, pp. 29--32 (2005) (in Japanese).

  5. 5.T. Nakanishi, S. Kishimoto, T. Sakurai and H. Kitagawa, A construction method of a metadata space based on relations between words from an index of a book, Proc. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM '05, Victoria, pp. 438-441 (2005).

  6. 6.T. Nakanishi, S. Kishimoto, T. Sakurai and T. Kitagawa, A construction method of a metadata space for a specific field from an index of a document, Trans. IEICE, Vol. J88, No. 4, pp. 840--851 (2005) (in Japanese).

  7. 7.M. Murakata, T. Otsuka, S. Kishimoto, T. Nakanishi, T. Sakurai, T. Kitagawa, Realization of the input support GUI for a complex mathematical formulae search, Proc. DEWS2005, CD-ROM (2005) (in Japanese).

  8. 8.T. Nakanishi, S. Kishimoto, T. Sakurai, T. Kitagawa,A construction method of a metadata space based on relations between words from an index of a book for a specific field, Proc. DEWS2005, CD-ROM (2005) (in Japanese).

  9. 9.T. Nakanishi, S. Kishimoto, T. Sakurai and T. Kitagawa, An integration and extension method of a metadata space from indexes of documents, DBSJ Letters. Vol. 3, No. 1, pp.141--144 (2004) (in Japanese).

  10. 10.T. Nakanishi, S. Kishimoto, T. Sakurai and T. Kitagawa, An integration and extension method of metadata spaces from indexes of documents, Proc. DEWS2004, CD-ROM (2004) (in Japanese).

  11. 11.S. Kishimoto, T. Nakanishi, T. Sakurai and T. Kitagawa, An implementation method of composite association retrieval for data of mathematical formulas with words, Proc. DEWS2004, CD-ROM (2004) (in Japanese).

  12. 12.T. Nakanishi, S. Kishimoto, T. Sakurai and T. Kitagawa, Metadata extraction method for a construction method of a metadata space based on pages of index for semantic associative search of a specific field, Proc. DBWeb2003, pp. 45--52 (2003) (in Japanese).

  13. 13.S. Kishimoto, T. Nakanishi, T. Sakurai, T. Kitagawa and T. Tochigi, An implementation method of similarity-based retrieval for formulas using MathML, Proc. DEWS2003, CD-ROM (2003) (in Japanese).

  14. 14.T. Sakurai, Y. Zhao, H. Sugiura and T. Torii, A front-end tool for mathematical computation and education in a network environment, Proc. ATCM'98, Tsukuba, pp.197-205 (1998).

  15. 15.Y. Zhao, T, Sakurai, H. Sugiura and T. Torii, Formalization and parsing of mathematical expressions for mathematical computation, Journal of Japan Society for Symbolic and Algebra Computation, Vol. 6, No. 3, pp. 2--29 (1998).

  16. 16.Y. Zhao, T. Sakurai, H. Sugiura and T. Torii, Notation extension methods in mathematical notation parsing for mathematical computation, Proc. ASCM'96, Kobe, pp.81--91 (1996).

  17. 17.Y. Zhao, T. Sakurai, H. Sugiura and T. Torii, A methodology of parsing mathematical notation for mathematical computation, Proc. ISSAC '96 Symposium, Zurich, pp. 292-300 (1996).

  18. 18.T. Sakurai, E. Zhao, T. Torii and H. Sugiura, A user interface for natural mathematical notations, Trans. Japan SIAM, Vol. 6, No. 1, pp. 147--157 (1996) (in Japanese).

  19. 19.E. Zhao, T. Torii, H. Sugiura and T. Sakurai, A knowledge-based method for mathematical notations understanding, Trans. Inform. Process. Soc. Japan, Vol. 35, No. 11, pp. 2366--2381 (1994).


Technical Reports

  1. 1.A. Imakura, L. Du, T. Sakurai, Accuracy analysis on the Rayleigh-Ritz type of the contour integral based eigensolver for solving generalized eigenvalue problems, Tech. Rep. Tsukuba, CS-TR-14-23, 2014.

  2. 2.H. Tadano, T. Sakurai, Y. Kuramashi, A new block Krylov subspace method for computing high accuracy solutions, CS-TR-08-16, Tsukuba, 2008.

  3. 3.J. Asakura, T. Sakurai, H. Tadano, T. Ikegami, K. Kimura, A numerical method for polynomial eigenvalue problems using contour integral, CS-TR-08-15, Tsukuba, 2008.

  4. 4.T. Sakurai, H. Tadano, T. Ikegami, U. Nagashima, A parallel eigensolver using contour integration for generalized eigenvalue problems in molecular simulation, CS-TR-08-14, Tsukuba, 2008.

  5. 5.I. Ikegami, T. Sakurai, U. Nagashima, A filter diagonalization for generalized eigenvalue problems based on the Sakurai-Sugiura projection method,CS-TR-08-13, Tsukuba, 2008.

  6. 6.T. Sakurai, H. Sugiura, A projection method for generalized eigenvalue problems, ISE-TR-02-189, Tsukuba, 2002.

  7. 7.T. Sakurai, P. Kravanja, H. Sugiura, M. Van Barel, An error analysis of two related quadrature methods for computing zeros of analytic functions, TW-334, K. U. Leuven, 2001.

  8. 8.T. Sakurai, H. Sugiura, On factorization of analytic functions and its verification, ISE-TR-99-162, Tsukuba, 1999.

 

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