| The AAA algorithm for rational approximation Y Nakatsukasa, O Sète, LN Trefethen SIAM Journal on Scientific Computing 40 (3), A1494-A1522, 2018 | 414 | 2018 |
| Stable and efficient spectral divide and conquer algorithms for the symmetric eigenvalue decomposition and the SVD Y Nakatsukasa, NJ Higham SIAM Journal on Scientific Computing 35 (3), A1325-A1349, 2013 | 127 | 2013 |
| Solving the trust-region subproblem by a generalized eigenvalue problem S Adachi, S Iwata, Y Nakatsukasa, A Takeda SIAM Journal on Optimization 27 (1), 269-291, 2017 | 96 | 2017 |
| Optimizing Halley's iteration for computing the matrix polar decomposition Y Nakatsukasa, Z Bai, F Gygi SIAM Journal on Matrix Analysis and Applications 31 (5), 2700-2720, 2010 | 83 | 2010 |
| Rational minimax approximation via adaptive barycentric representations SI Filip, Y Nakatsukasa, LN Trefethen, B Beckermann SIAM Journal on Scientific Computing 40 (4), A2427-A2455, 2018 | 79 | 2018 |
| Rational neural networks N Boullé, Y Nakatsukasa, A Townsend Advances in neural information processing systems 33, 14243-14253, 2020 | 74 | 2020 |
| Computing fundamental matrix decompositions accurately via the matrix sign function in two iterations: The power of Zolotarev's functions Y Nakatsukasa, RW Freund siam REVIEW 58 (3), 461-493, 2016 | 67 | 2016 |
| Roundoff error analysis of the CholeskyQR2 algorithm Y Yamamoto, Y Nakatsukasa, Y Yanagisawa, T Fukaya Electron. Trans. Numer. Anal 44 (01), 306-326, 2015 | 61 | 2015 |
| Vandermonde with arnoldi PD Brubeck, Y Nakatsukasa, LN Trefethen Siam Review 63 (2), 405-415, 2021 | 60 | 2021 |
| Classification of chaotic time series with deep learning N Boullé, V Dallas, Y Nakatsukasa, D Samaddar Physica D: Nonlinear Phenomena 403, 132261, 2020 | 59 | 2020 |
| Fast and stable randomized low-rank matrix approximation Y Nakatsukasa arXiv preprint arXiv:2009.11392, 2020 | 56 | 2020 |
| Vector spaces of linearizations for matrix polynomials: a bivariate polynomial approach Y Nakatsukasa, V Noferini, A Townsend SIAM Journal on Matrix Analysis and Applications 38 (1), 1-29, 2017 | 53* | 2017 |
| A theory of quantum subspace diagonalization EN Epperly, L Lin, Y Nakatsukasa SIAM Journal on Matrix Analysis and Applications 43 (3), 1263-1290, 2022 | 52 | 2022 |
| Shifted Cholesky QR for computing the QR factorization of ill-conditioned matrices T Fukaya, R Kannan, Y Nakatsukasa, Y Yamamoto, Y Yanagisawa SIAM Journal on Scientific Computing 42 (1), A477-A503, 2020 | 51 | 2020 |
| CholeskyQR2: a simple and communication-avoiding algorithm for computing a tall-skinny QR factorization on a large-scale parallel system T Fukaya, Y Nakatsukasa, Y Yanagisawa, Y Yamamoto 2014 5th workshop on latest advances in scalable algorithms for large-scale …, 2014 | 51 | 2014 |
| Fast graph sampling set selection using gershgorin disc alignment Y Bai, F Wang, G Cheung, Y Nakatsukasa, W Gao IEEE Transactions on signal processing 68, 2419-2434, 2020 | 47 | 2020 |
| Exponential node clustering at singularities for rational approximation, quadrature, and PDEs LN Trefethen, Y Nakatsukasa, JAC Weideman Numerische Mathematik 147, 227-254, 2021 | 45 | 2021 |
| An algorithm for real and complex rational minimax approximation Y Nakatsukasa, LN Trefethen SIAM Journal on Scientific Computing 42 (5), A3157-A3179, 2020 | 43 | 2020 |
| Solving generalized CDT problems via two-parameter eigenvalues S Sakaue, Y Nakatsukasa, A Takeda, S Iwata SIAM Journal on Optimization 26 (3), 1669-1694, 2016 | 43* | 2016 |
| Computing the common zeros of two bivariate functions via Bézout resultants Y Nakatsukasa, V Noferini, A Townsend Numerische Mathematik 129 (1), 181-209, 2015 | 43 | 2015 |
LN TrefethenProfessor of Applied Mathematics in Residence, Harvard University在 seas.harvard.edu 的电子邮件经过验证
