International Conference On Preconditioning Techniques
For Scientific and Industrial Applications

June 10—12, 2024
Georgia Institute of Technology
Atlanta, USA

Conference Program

The International Conference on Preconditioning Techniques for Scientific and Industrial Applications, Preconditioning 2024, is the thirteenth in a series of conferences that focus on preconditioning techniques for sparse and structured matrix computations. As problem sizes and problem complexity continue to increase and as new applications emerge, such as those in inverse problems and machine learning, preconditioning techniques become increasingly important and must also adapt. The conference brings together researchers developing new methods and tools, along with scientists and engineers addressing complex issues of applying preconditioning techniques in large-scale and industrial settings.

Panel and Tutorial on Machine Learning + Preconditioning

The conference will feature a plenary panel discussion on the role of machine learning in the field of preconditioning. The panelists will first present a survey or tutorial on the potential of using machine learning for preconditioning (and vice-versa) before opening the floor for a general discussion on exploiting the synergies between machine learning and preconditioning.

Conference Themes

The themes to be covered at the conference include, but are not limited to, the following:

  • Nonlinear preconditioning
  • Machine learning for preconditioning and preconditioning for machine learning
  • Preconditioning for inverse problems
  • Randomized algorithms for preconditioning
  • Mixed-precision preconditioning
  • High-performance and parallel preconditioning and software
  • Preconditioning for data assimilation
  • Domain decomposition and multigrid methods
  • Physics-based preconditioners
  • Algebraic preconditioners: incomplete factorizations and sparse approximate inverses
  • Preconditioning for indefinite and saddle point problems
  • Inexact solvers
  • Hierarchical matrices and structured matrix approximation

Invited Speakers

Monica Dessole
CERN SFT-EP, Switzerland

Selime Gürol
CERFACS, France

Alexander Heinlein
Delft University of Technology, Netherlands

Rui Peng Li
Lawrence Livermore National Laboratory, USA

Will Pazner
Portland State University, USA

Tyrone Rees
STFC, UK

Jacob B. Schroder
University of New Mexico, USA

Organizers

Conference Chairs

  • Esmond G. Ng, Lawrence Berkeley National Laboratory, USA
  • Yousef Saad, University of Minnesota, USA
  • Andy Wathen, University of Oxford, UK

Local Organization Committee

  • Edmond Chow, Georgia Institute of Technology, USA
  • Yuanzhe Xi, Emory University, USA

Program Committee

  • Edmond Chow (co-chair, SIAM rep.), Georgia Institute of Technology, USA
  • Yuanzhe Xi (co-chair), Emory University, USA
  • Hartwig Anzt, University of Tennessee, USA
  • Michele Benzi, Scuola Normale Superiore, Italy
  • Matthias Bollhoefer, TU Braunschweig, Germany
  • Erik Boman, Sandia National Laboratories, USA
  • Jed Brown, University of Colorado Boulder, USA
  • Xiao-Chuan Cai, University of Macau, China
  • Victorita Dolean, TU Eindhoven, Netherlands
  • Melina Freitag, University of Potsdam, Germany
  • Chen Greif, University of British Columbia, Canada
  • Luke Olson, University of Illinois at Urbana-Champaign, USA
  • Daniel Osei-Kuffuor, Lawrence Livermore National Laboratory, USA
  • John Pearson, University of Edinburgh, Scotland
  • Wil Schilders, TU Eindhoven, Netherlands
  • Nicole Spillane, CNRS, Ecole Polytechnique, France
  • Martin Stoll, TU Chemnitz, Germany
  • Miroslav Tuma, Charles University, Czech Republic
  • Qiang Ye, University of Kentucky, USA
  • Irad Yavneh, Technion, Israel
  • Rio Yokota, Tokyo Institute of Technology, Japan

Sponsors

Sponsors