A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Author:
Funder
National Natural Science Foundation of China
Science Challenge Project
Science Fund for Distinguished Young Scholars of Sichuan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00357-024-09484-0.pdf
Reference44 articles.
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2. Baker, A. H., Jessup, E. R., & Manteuffel, T. (2005). A technique for accelerating the convergence of restarted GMRES. SIAM Journal on Matrix Analysis and Applications, 26, 962–984.
3. Benzi, M. (2002). Preconditioning techniques for large linear systems: A survey. Journal of Computational Physics, 182, 418–477.
4. Bhowmick, S., Eijkhout, V., Freund, Y., Fuentes, E., & Keyes, D. (2000). Application of machine learning in selecting sparse linear solvers. Astronomical Journal, 119(2), 936–944.
5. Bhowmick, S., Eijkhout, V., Freund, Y., Fuentes, E., & Keyes, D. (2011). Application of alternating decision trees in selecting sparse linear solvers. New York: Springer.
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