論文等

 

最近の論文

  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 xomplex 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.関川悠太, 二村保徳, 今倉暁, 櫻井鉄也, 複数右辺ベクトルを持つシフト線形方程式に対する多項式前処理の有効性の検証, 日本応用数理学会論文誌, 29, 141-164 (2019). 

  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).

著書

  1. 1. 固有値計算と特異値計算, 共著, 日本計算工学会 計算力学レクチャーコース, 丸善出版 (2019), ISBN9784621304730

  2. 2.数値線形代数の数理とHPC ,共著, 日本応用数理学会監修, 固有値問題の数値解法, シリーズ応用数理6巻, 共立出版 (2018), ISBN978-4-320-01955-3.

  3. 3.数値の処理と数値解析, 単著, 放送大学出版会 (2014), ISBN-13 978-4595315046.

  4. 4.計算力学理論ハンドブック, 共著, 朝倉書店 (2010), ISBN978-4-254-23120-5.

  5. 5.現代数理科学事典 第2版, 共著, 丸善 (2009) ISBN978-4-621-08125-9.

  6. 6.数値計算のわざ, 共著, 共立出版 (2006) ISBN4-320-01803-6.

  7. 7.数値計算のつぼ, 共著, 共立出版 (2004) ISBN4-320-12088-4.

  8. 8.MATLAB/Scilabで理解する数値計算, 単著, 東京大学出版会 (2003) ISBN4-13-062450-4.

  9. 9.数値計算法, 共著, 新コンピュータサイエンス講座, オーム社 (1998) ISBN4-274-13153-X.

論文リスト（関連キーワードごと）

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).


固有値問題, 並列計算, アプリケーション

  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, 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 Paper, 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.前田恭行, 櫻井鉄也, 周回積分を用いた固有値解法の円弧領域に対する拡張, 情報処理学会論文誌コンピューティングシステム, 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 and 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 and 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 and 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 and 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 and 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.

  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., 233, 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.木原崇智, 多田野寛人, 櫻井鉄也, 精度混合型Krylov部分空間反復法における疎行列ベクトル積のCell BE上での実装と性能評価, 情報処理学会論文誌 コンピューティングシステム, Vol. 1, No. 1, pp. 51-60 (2008).

  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, U. Nagashima, Molecular orbital calculation for large molecule, J. Comput. Chem. Japan, Vol. 6 No. 3, pp. 217-226 (2007).

  51. 51.先崎健太，多田野寛人，櫻井鉄也, AMLS法による固有値分布の推定, 日本応用数理学会論文誌, Vol. 17, pp. 511-521 (2007).

  52. 52.T. Sakurai and 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.相田祥昭, 中島佳宏, 佐藤三久, 建部修見, 櫻井鉄也, Grid RPCにおける広域データ管理レイヤの利用, ACS論文誌, Vol. 48, No. SIG8, pp. 127-144 (2007).

  54. 54.T. Sakurai, Y. Kodaki, H. Tadano, H. Umeda, Y. Inadomi, T. Watanabe, 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.木原崇智, 小瀧義久, 多田野寛人, 櫻井鉄也,  GridRPC/MPIハイブリッドによる修正多重リスタート付きArnoldi法, 情報処理学会ACS論文誌, Vol. 48, No. SIG8, pp. 94-103 (2007).

  56. 56.T. Watanabe, Y. Inadomi, T. Ishimoto, H. Umeda, T. Sakurai, 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, Lecture Notes in Computer Science, No. 3743, pp. 438-445 (2006).

  59. 59.稲富雄一, 梅田宏明, 渡邊寿雄, 櫻井鉄也, 長嶋雲兵, FMO-MO法における大規模分子軌道計算 -- 固有値分布の特徴 --, 日本応用数理学会論文誌, Vol. 15, No. 2, pp. 169--179 (2005).

  60. 60.稲富雄一, 梅田宏明, 渡邊寿雄, 櫻井鉄也, 長嶋雲兵, FMO-MO法による大規模分子軌道計算, 情報処理学会ACS論文誌, Vol. 46, No. 10, pp. 1-8 (2005).

  61. 61.櫻井鉄也, 多田野寛人, 早川賢太郎, 佐藤三久,  高橋大介, 長嶋雲兵, 稲富雄一, 梅田宏明, 渡邊寿雄, 大規模固有値問題のmaster-worker型並列解法, 情報処理学会ACS論文誌, Vol. 46, No. 10, pp. 44-51 (2005).

  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, 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.小笠原匡, 多田野寛人, 櫻井鉄也, 伊藤祥司, Shifted Linear Systemsに対するKrylov部分空間反復法と固有値問題への応用, 日本応用数理学会論文誌, Vol. 14, No. 3, pp. 193-205 (2004).

  66. 66.櫻井鉄也, 大規模固有値問題の並列解法, 応用数理, Vol. 13, No. 4, pp. 308-317 (2003).

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


テンソル計算, 線形方程式の解法, Krylov部分空間法，前処理法，QCDシミュレーション

  1. 1.関川悠太, 二村保徳, 今倉暁, 櫻井鉄也, 複数右辺ベクトルを持つシフト線形方程式に対する多項式前処理の有効性の検証, 日本応用数理学会論文誌, 29, 141-164 (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, No. 1, 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.多田野寛人, 櫻井鉄也, 周回積分法に対するBlock Krylov部分空間反復法の適用と分子軌道計算への応用, 情報処理学会論文誌 コンピューティングシステム, Vol. 2 No. 2, pp. 10-18 (2009).

  15. 15.山崎育朗, 岡田真幸, 多田野寛人, 櫻井鉄也, 寺西慶太, Cutoffを2重に用いた前処理の性能評価, 日本応用数理学会論文誌, Vol. 18, No. 4, pp. 631-651 (2008).

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

  17. 17.岡田真幸, 櫻井鉄也, 寺西慶太, 近似係数行列に対する疎行列用直接解法を用いた前処理, 日本応用数理学会論文誌, Vol. 17, No. 3, pp. 319-329 (2007).

  18. 18.岡田真幸, 多田野寛人, 櫻井鉄也, 複素対称行列に対する前処理の評価方法について, 日本応用数理学会論文誌, Vol. 16, No. 4, pp.497-505 (2006).

  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.多田野寛人, 櫻井鉄也, LanczosプロセスのリスタートによるCGS法の安定化, 日本応用数理学会論文誌, Vol.15, No. 2, pp. 85-99 (2005).

  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.櫻井鉄也, 高林記誉宣, 名取亮, ランチョス多項式の漸化式計算における数値的不安定性の回避法, 情報処理学会論文誌, Vol. 40, No. 12, pp. 4169-4177 (1999).

  24. 24.金成海, 張紹良, 名取亮, 櫻井鉄也, 周偉東, MCGS法：非対称連立一次方程式のための新しい反復解法, 情報処理学会論文誌, Vol. 37, No. 11, pp. 2138-2141 (1996).


非線形方程式, 零点問題, 因子法, 区間演算, 精度保証計算, 有理関数近似

  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.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).

  4. 4.牛暁明, 櫻井鉄也, 杉浦洋, 解析関数の多項式因子を求める精度保証付き解法, 日本応用数理学会論文誌, Vol. 14, No. 1, pp. 59-73 (2004).

  5. 5.牛暁明, 櫻井鉄也, 固有値解法による代数方程式の重根を求める方法, 応用数理学会論文誌, Vol. 13, No. 4, pp. 447-460 (2003).

  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.大迫尚行, 鳥居達生, 杉浦洋, 櫻井鉄也, 多項式剰余列の安定な生成法, 日本応用数理学会論文誌, Vol. 5, No. 3, pp. 241-255 (1995).

  19. 19.櫻井鉄也, 杉浦洋, 鳥居達生, Durand-Kerner型補助関数を用いた非線形方程式の多段反復解法, 日本応用数理学会論文誌, Vol. 4, No. 2, pp. 67-80 (1994).

  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.園田信吾, 櫻井鉄也, 杉浦洋, 鳥居達生, 分割統治法による多項式の因数分解, 日本応用数理学会論文誌, Vol. 1, No. 4, pp. 277-290 (1991).

  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.櫻井鉄也, 鳥居達生, 杉浦洋, 高次収束する代数方程式の全根同時反復解法, 情報処理学会論文誌, Vol. 31, No. 7, pp. 964-969 (1990).

  27. 27.櫻井鉄也, 鳥居達生, 杉浦洋, Pade近似による代数方程式の反復解法, 情報処理学会論文誌, Vol. 31, No. 4, pp. 517-522 (1990).

  28. 28.櫻井鉄也, 杉浦洋, 鳥居達生, 静電場的解釈による代数方程式の反復解法, 情報処理学会論文誌, Vol. 29, No. 5, pp. 447-455 (1988).


数値等角写像, パデ近似

  1. 1.呂毅斌, 伊東拓, 櫻井鉄也, 多重連結領域数値等角写像のPade近似を用いた電荷点配置法, 日本応用数理学会論文誌, Vol. 16, No. 3, pp. 149-164 (2006).

  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.呂毅斌, 伊東拓, 伊藤祥司, 櫻井鉄也, Pade近似を用いた数値等角写像計算のArnoldi法による精度改善, 日本応用数理学会論文誌, Vol. 15, No. 3, pp. 495-500 (2005).

  4. 4.櫻井鉄也, 杉浦洋, Pade近似を用いた数値等角写像の計算法, 情報処理学会論文誌, Vol. 43, No. 9, pp. 2959-2962 (2002).

  5. 5.宋殷志, 杉浦洋, 櫻井鉄也, 等角写像に関するWegmannの方法の不安定性の解析とその安定化, 情報処理学会論文誌, Vol. 32, No. 2, pp. 126-132 (1991).

  6. 6.宋殷志, 杉浦洋, 櫻井鉄也, 数値等角写像におけるTeodorsen方程式の解法, 情報処理学会論文誌, Vol. 30, No. 4, pp. 393-401 (1989).


逆問題, 複素モーメント, EEG

  1. 1.伊藤信貴, 奈良高明, 櫻井鉄也, 複素モーメントに基づく画像特徴抽出, 日本応用数理学会論文誌, Vol. 18, No. 1, pp. 135-153 (2008).

  2. 2.大濱潤二, 櫻井鉄也, 奈良高明, 双極子推定逆問題に対する直接解法の誤差評価, 日本応用数理学会論文誌, Vol. 15, No. 3, pp. 483-494 (2005).


数値積分, 積分公式

  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).


データマイニング, ベクトル空間法, 問題解決環境, MathML, GUI, 数理ソフトウエア

  1. 1.岸本貞弥, 村方衛, 中西崇文, 櫻井鉄也, 北川高嗣, 数理分野を対象とした問題解決支援システム, 第18回データ工学ワークショップ(DEWS2007), 電子情報通信学会, 電子版 (2007).

  2. 2.村方衛, 岸本貞弥, 大塚透, 中西崇文, 櫻井鉄也, 北川高嗣, 複合数式検索を対象とした入力支, GUI"MathGUIde"の実現, 第17回データ工学ワークショップ(DEWS2006), 電子情報通信学会, 電子版 (2006).

  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.中西崇文, 岸本貞弥, 村方衛, 大塚透, 北川高嗣, 数式データを対象とした複合連想検索システムの実現, 日本データベース学会Letters Vol.4, No.1, pp.29-32 (2005).

  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.中西崇文, 岸本貞弥, 櫻井鉄也, 北川高嗣, 特定分野を対象とした連想検索のための書籍の索引部を用いたメタデータ空間生成方式, 電子情報通信学会論文誌, Vol. J88, No. 4, pp. 840-851 (2005).

  7. 7.岸本貞弥, 中西崇文, 村方衛, 大塚透, 櫻井鉄也, 北川高嗣, 数式データを対象とした複合連想検索システム, 第16回データ工学ワークショップ(DEWS2005), 電子情報通信学会, 電子版 (2005).

  8. 8.村方衛, 大塚透, 岸本貞弥, 中西崇文, 櫻井鉄也, 北川高嗣, 複合数式検索を対象とした入力支援GUIの実現, 第16回データ工学ワークショップ(DEWS2005), 電子情報通信学会, 電子版 (2005).

  9. 9.中西崇文, 岸本貞弥, 櫻井鉄也, 北川高嗣, 複数の書籍の索引部を用いたメタデータ空間拡張統合方式, 日本データベース学会Letters, Vol. 3, No. 1, pp. 141--144 (2004).

  10. 10.中西崇文, 岸本貞弥, 櫻井鉄也, 北川高嗣, 複数の書籍の索引部を用いたメタデータ空間拡張統合方式, 第15回データ工学ワークショップ(DEWS2004)論文集, 電子情報通信学会, 電子版 (2004).

  11. 11.岸本貞弥, 中西崇文, 櫻井鉄也, 北川高嗣, 数式データを対象とした複合連想検索の実現, 第15回データ工学ワークショップ(DEWS2004)論文集, 電子情報通信学会, 電子版 (2004).

  12. 12.中西崇文, 岸本貞弥, 櫻井鉄也, 北川高嗣, 特定分野を対象とした連想検索のためのページベースのメタデータ空間生成方式, データベースとWeb情報システムに関するシンポジウム(DBWeb2003)論文集, 電子情報通信学会, pp. 45--52 (2003)．

  13. 13.岸本貞弥, 中西崇文, 櫻井鉄也, 北川高嗣, 栃木敏子, MathMLを用いた類似数式検索方式の実現 , 第14回データ工学ワークショップ(DEWS2003)論文集, 電子情報通信学会, 電子版 (2003)

  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, 数式処理, 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.櫻井鉄也, 趙燕結, 鳥居達生, 杉浦洋, 自然な数学表記のためのユーザインターフェイス, 日本応用数理学会論文誌, Vol. 6, No. 1, pp. 147--157 (1996).

  19. 19.Y. Zhao, T. Torii, H. Sugiura and T. Sakurai, A knowledge-based method for mathematical notations understanding, 情報処理学会論文誌, Vol. 35, No. 11, pp. 2366--2381 (1994).


Technical Reports

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

  2. 2.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.

  3. 3.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.

  4. 4.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.

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

  6. 6.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.

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