Preconditioning Techniques for Scientific and Industrial Applications, Preconditioning 2026, is part of a series of international conferences focusing on preconditioning techniques for sparse and structured matrix computations. The key focus of the meeting is meeting timely challenges in mathematical computation with novel, rigorous, and creative iterative linear algebra approaches for scientific applications. Key current areas of active research include theoretical and practical questions related to randomized preconditioning, probabilistic interpretations, the design of efficient tensor-based methods, preconditioners tailored to modern HPC architectures, and physics-based preconditioners for solving extremely large-scale systems in modelling and simulations, to name a few. The meeting 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.
We highlight that the week following this meeting, the SIAM Conference on Optimization will be held in Edinburgh (https://www.siam.org/conferences-events/siam-conferences/op26/), which connects to a number of themes of this meeting.
Themes of the Meeting
Machine learning for preconditioning and preconditioning for machine learning;
Randomized algorithms for preconditioning;
Mixed-precision preconditioning;
High-performance and parallel preconditioning and software;
Preconditioning for inverse problems;
Preconditioning for differential equations;
Preconditioning for data assimilation;
Nonlinear preconditioning;
Physics-based preconditioners;
Quantum numerical linear algebra and preconditioning;
Domain decomposition and multigrid methods;
Algebraic preconditioners: incomplete factorisations and sparse approximate inverses;
Preconditioning for indefinite and saddle-point problems;
Hierarchical matrices and structured matrix approximation;
Industrial applications of preconditioning
Programme Committee
Erin Carson (Charles University)
Edmond Chow (Georgia Institute of Technology)
Monica Dessole (CERN SFT-EP)
Victorita Dolean (TU Eindhoven)
Melina Freitag (University of Potsdam)
Selime Gürol (CERFACS)
Chen Greif (University of British Columbia)
Scott MacLachlan (Memorial University of Newfoundland)
Daniel Osei-Kuffuor (Lawrence Livermore National Laboratory)
Wil Schilders (TU Eindhoven)
Jacob Schroder (University of New Mexico)
Nicole Spillane (CMAP, École Polytechnique)
Martin Stoll (TU Chemnitz)
Ray Tuminaro (Sandia National Laboratories)
Yuanzhe Xi (Emory University)
Information on participation to follow in due course.
