Ken Hayami

855 total citations
39 papers, 547 citations indexed

About

Ken Hayami is a scholar working on Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Ken Hayami has authored 39 papers receiving a total of 547 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Atomic and Molecular Physics, and Optics, 20 papers in Computational Theory and Mathematics and 16 papers in Computational Mechanics. Recurrent topics in Ken Hayami's work include Electromagnetic Scattering and Analysis (20 papers), Matrix Theory and Algorithms (19 papers) and Advanced Numerical Methods in Computational Mathematics (8 papers). Ken Hayami is often cited by papers focused on Electromagnetic Scattering and Analysis (20 papers), Matrix Theory and Algorithms (19 papers) and Advanced Numerical Methods in Computational Mathematics (8 papers). Ken Hayami collaborates with scholars based in Japan, China and Canada. Ken Hayami's co-authors include Jun-Feng Yin, Ning Zheng, Guoqiang Han, Jiong Wang, Kōkichi Sugihara, Yasunori Aoki, Takumi Washio, Masaaki Sugihara, Lothar Reichel and Akihiko Konagaya and has published in prestigious journals such as Energies, IEEE Transactions on Magnetics and Applied Mathematics and Computation.

In The Last Decade

Ken Hayami

38 papers receiving 494 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ken Hayami Japan 14 185 181 156 149 147 39 547
Rodrigo B. Platte United States 13 89 0.5× 79 0.4× 203 1.3× 101 0.7× 242 1.6× 30 545
Manfred R. Trummer Canada 11 145 0.8× 76 0.4× 115 0.7× 243 1.6× 165 1.1× 38 584
Quôc Thông Lê Gia Australia 14 126 0.7× 74 0.4× 171 1.1× 129 0.9× 197 1.3× 55 537
Tobias von Petersdorff United States 17 287 1.6× 259 1.4× 406 2.6× 116 0.8× 356 2.4× 23 883
Luis G. Reyna United States 13 135 0.7× 137 0.8× 70 0.4× 87 0.6× 203 1.4× 28 597
Wilhelm Heinrichs Germany 15 122 0.7× 56 0.3× 120 0.8× 229 1.5× 369 2.5× 50 560
Giorgio Pini Italy 16 381 2.1× 280 1.5× 181 1.2× 109 0.7× 350 2.4× 65 654
Michael Karkulik Chile 11 98 0.5× 69 0.4× 212 1.4× 55 0.4× 251 1.7× 26 408
Jan Brandts Netherlands 14 235 1.3× 47 0.3× 212 1.4× 247 1.7× 480 3.3× 46 747
Kenneth L. Bowers United States 13 154 0.8× 67 0.4× 163 1.0× 362 2.4× 145 1.0× 25 685

Countries citing papers authored by Ken Hayami

Since Specialization
Citations

This map shows the geographic impact of Ken Hayami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ken Hayami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ken Hayami more than expected).

Fields of papers citing papers by Ken Hayami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ken Hayami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ken Hayami. The network helps show where Ken Hayami may publish in the future.

Co-authorship network of co-authors of Ken Hayami

This figure shows the co-authorship network connecting the top 25 collaborators of Ken Hayami. A scholar is included among the top collaborators of Ken Hayami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ken Hayami. Ken Hayami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hayami, Ken, et al.. (2022). GMRES using pseudoinverse for range symmetric singular systems. Journal of Computational and Applied Mathematics. 422. 114865–114865.
2.
Hansen, Per Christian, et al.. (2022). GMRES methods for tomographic reconstruction with an unmatched back projector. Journal of Computational and Applied Mathematics. 413. 114352–114352. 9 indexed citations
3.
Hayami, Ken, et al.. (2020). Right preconditioned MINRES for singular systems†. Numerical Linear Algebra with Applications. 27(3). 3 indexed citations
4.
Millar, Robert John, et al.. (2020). Slime Mold Inspired Distribution Network Initial Solution. Energies. 13(23). 6278–6278. 4 indexed citations
5.
Tsuchiya, Takashi, et al.. (2019). Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning. Terrestrial Environment Research Center (University of Tsukuba). 10 indexed citations
6.
Buccini, Alessandro, et al.. (2016). Modulus-based iterative methods for constrained Tikhonov regularization. Journal of Computational and Applied Mathematics. 319. 1–13. 28 indexed citations
7.
Zheng, Ning, Ken Hayami, & Jun-Feng Yin. (2016). Modulus-Type Inner Outer Iteration Methods for Nonnegative Constrained Least Squares Problems. SIAM Journal on Matrix Analysis and Applications. 37(3). 1250–1278. 28 indexed citations
8.
Hayami, Ken, et al.. (2015). Improvements to the cluster Newton method for underdetermined inverse problems. Journal of Computational and Applied Mathematics. 283. 122–141. 6 indexed citations
9.
Aoki, Yasunori, Ken Hayami, Hans De Sterck, & Akihiko Konagaya. (2014). Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics. SIAM Journal on Scientific Computing. 36(1). B14–B44. 20 indexed citations
10.
Hayami, Ken, et al.. (2013). Inner-Iteration Krylov Subspace Methods for Least Squares Problems. SIAM Journal on Matrix Analysis and Applications. 34(1). 1–22. 20 indexed citations
11.
Aoki, Yasunori, Ken Hayami, Hans De Sterck, & Akihiko Konagaya. (2011). Cluster newton method for sampling multiple solutions of an underdetermined inverse problem: Parameter identification for pharmacokinetics. 1–38. 7 indexed citations
12.
Hayami, Ken, et al.. (2011). Greville’s method for preconditioning least squares problems. Advances in Computational Mathematics. 35(2-4). 243–269. 6 indexed citations
13.
Yin, Jun-Feng & Ken Hayami. (2008). Preconditioned GMRES methods with incomplete Givens orthogonalization method for large sparse least-squares problems. Journal of Computational and Applied Mathematics. 226(1). 177–186. 9 indexed citations
14.
Hayami, Ken, et al.. (2003). Boundary element simulation of large amplitude standing waves in vessels. Engineering Analysis with Boundary Elements. 27(6). 565–574. 12 indexed citations
15.
Han, Guoqiang, et al.. (2000). Correction method and extrapolation method for singular two-point boundary value problems. Journal of Computational and Applied Mathematics. 126(1-2). 145–157. 11 indexed citations
16.
Hayami, Ken, et al.. (1998). Panel Clustering For 3-D Elastostatics Using Spherical Harmonics. WIT transactions on modelling and simulation. 21. 6 indexed citations
17.
Hayami, Ken & Stefan Sauter. (1998). Cost Estimation Of The Panel ClusteringMethod Applied To 3-D Elastostatics. WIT transactions on modelling and simulation. 20. 5 indexed citations
18.
Hayami, Ken, et al.. (1994). A numerical quadrature for nearly singular boundary element integrals. Engineering Analysis with Boundary Elements. 13(2). 143–154. 66 indexed citations
19.
Hayami, Ken, et al.. (1994). Improvement of quadrature for nearly singular integrals in 3D-BEM. 4 indexed citations
20.
Hayami, Ken. (1990). High precision numerical integration methods for 3-D boundary element analysis (of electromagnetic problems). IEEE Transactions on Magnetics. 26(2). 603–606. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026