Articles dans des revues avec comité de lecture (24)

  1. 1. Boukhris, S., Napov, A., & Notay, Y. (2023). Algebraic multigrid using a stencil-CSR hybrid format on GPUs. SIAM journal on scientific computing, 45(3), C154-C178.
  2. 2. Napov, A. (2023). An incomplete Cholesky preconditioner based on orthogonal approximations. SIAM journal on scientific computing, 54(2), 729-752. doi:10.1137/21M1468334
  3. 3. Bacq, P.-L., Gounand, S., Napov, A., & Notay, Y. (2023). An all-at-once algebraic multigrid method for finite element discretizations of Stokes problem. International journal for numerical methods in fluids, 95, doi: 10.1002/fld.5145, 193-214.
  4. 4. Moro, F., Napov, A., Pellikka, M., Smajic, J., & Codecasa, L. (2022). Fast Solution of 3-D Eddy-Current Problems in Multiply Connected Domains by a, v-φ and t-φ Formulations With Multigrid-Based Algorithm for Cohomology Generation. IEEE access, 10, 112416 - 112432. doi:10.1109/ACCESS.2022.3216876
  5. 5. El Haman Abdeselam, A., Napov, A., & Notay, Y. (2022). Porting an aggregation-based algebraic multigrid method to GPUs. Electronic transactions on numerical analysis, 55, 687-705.
  6. 6. Moro, F., Napov, A., & Codecasa, L. (2021). A Hybrid a-ϕ Cell Method for Solving Eddy-Current Problems in 3-D Multiply-Connected Domains. IEEE access, 9, 158247-158260. doi:10.1109/ACCESS.2021.3130676
  7. 7. Napov, A., & Perrussel, R. (2019). Algebraic analysis of two-level multigrid methods for edge elements. Electronic transactions on numerical analysis, 51, 387-411. doi:10.1553/etna_vol51s387
  8. 8. Napov, A., & Perrussel, R. (2019). Revisiting aggregation-based multigrid for edge finite elements. Electronic transactions on numerical analysis, 51, 118-134. doi:10.1553/etna_vol51s118
  9. 9. Napov, A. (2017). A divide-and-conquer bound for aggregate's quality and algebraic connectivity. Discrete mathematics, 340(10), 2355-2365. doi:10.1016/j.disc.2017.05.003
  10. 10. Napov, A., & Notay, Y. (2017). An efficient multigrid method for graph Laplacian systems II: robust aggregation. SIAM journal on scientific computing, 39(5), S379-S403. doi:10.1137/16M1071420
  11. 11. Napov, A., & Li, X. (2016). An algebraic multifrontal preconditioner that exploits the low-rank property. Numerical linear algebra with applications, 23, 61-82. doi:10.1002/nla.2006
  12. 12. Rouet, F.-H., Li, X., Ghysels, P., & Napov, A. (2016). A distributed-memory package for dense Hierarchically Semi-Separable matrix computations using randomization. ACM transactions on mathematical software, 42(4), 27. doi:10.1145/2930660

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