In lattice quantum chromodynamics (QCD) computations a substantial amount of work is
spent in solving discretized versions of the Dirac equation. Conventional Krylov solvers …

For various applications, it is well-known that a multi-level, in particular two-level,
preconditioned CG (PCG) method is an efficient method for solving large and sparse linear …

Advancements in the field of high-performance scientific computing are necessary to
address the most important challenges we face in the 21st century. From physical modeling …

We consider deflation and augmentation techniques for accelerating the convergence of
Krylov subspace methods for the solution of nonsingular linear algebraic systems. Despite …

Coarse-grid correction is a key ingredient of scalable domain decomposition methods. In
this work we construct coarse-grid space using the low-frequency modes of the subdomain …

Deflating the shifted Laplacian with geometric multigrid vectors yields speedup. To verify this
claim, we investigate a simplified variant of Erlangga and Nabben presented in [Erlangga …

It is well known that two-level and multilevel preconditioned conjugate gradient (PCG)
methods provide efficient techniques for solving large and sparse linear systems whose …

We show how to optimize rolling stock rotations that are required for the operation of a
passenger timetable. The underlying mathematical ptimization problem is called rolling …

Deflation is a well-known technique to accelerate Krylov subspace methods for solving
linear systems of equations. In contrast to preconditioning, in deflation methods singular …

Solving sparse linear systems from discretized partial differential equations is challenging.
Direct solvers have, in many cases, quadratic complexity (depending on geometry), while …