Hussam Al Daas

Computational Mathematician

I prepared my Ph.D. at Inria-Paris and Sorbonne University (Paris-VI). It was funded by TOTAL S.A. From January 2019 to July 2020 I was a Max Planck Society postdoctoral research fellow in the numerical linear and multilinear algebra team, a subgroup of the computational methods in control and system theory group. Starting from August 2020, I hold a research position at the Computational Mathematics Group at STFC Rutherford Appleton Laboratory.

  • High performance computing
  • Low-rank tensor methods
  • Krylov subspace methods
  • Domain decomposition

ORCID id: 0000-0001-9355-4042

Preprints:

  • Two-level Nyström--Schur preconditioner for sparse symmetric positive definite matries. 2021. PDF
    H. Al Daas, T. Rees, J. Scott

  • Parallel Algorithms for Tensor Train Arithmetic. 2020. PDF
    H. Al Daas, G. Ballard, P. Benner

  • Low-Rank and Total Variation Regularization and Its Application to Image Recovery. 2020. PDF
    P. Goyal, H. Al Daas, P. Benner

Publications:
  • A Multilevel Schwarz Preconditioner Based on a Hierarchy of Robust Coarse Spaces. 2019.
    SIAM Journal for Scientific Computing, in press, 2021. PDF
    H. Al Daas, L. Grigori, P. Jolivet, P. H. Tournier
    Code for reproducing the results available here, PETSc interface through the HPDDM package, PCHPDDM.

  • Recycling Krylov Subspaces and Truncating Deflation Subspaces for Solving Sequence of Linear Systems.
    ACM Transactions on Mathematical Software, in press, 2020. Research report
    H. Al Daas, L. Grigori, P. Henon, P. Ricoux

  • A Class of Efficient Locally Constructed Preconditioners Based on Coarse Spaces
    SIAM Journal on Matrix Analysis and Applications Vol. 40, 2019, pp. 66–91. PDF
    H. Al Daas, L. Grigori

  • Enlarged GMRES for solving linear systems with one or multiple right-hand sides
    IMA Journal of Numerical Analysis, Vol. 39, 2019, pp. 1924–1956.2019. PDF
    H. Al Daas, L. Grigori, P. Henon, P. Ricoux

RCUK