2,336 research outputs found
Objective acceleration for unconstrained optimization
Acceleration schemes can dramatically improve existing optimization
procedures. In most of the work on these schemes, such as nonlinear Generalized
Minimal Residual (N-GMRES), acceleration is based on minimizing the
norm of some target on subspaces of . There are many numerical
examples that show how accelerating general purpose and domain-specific
optimizers with N-GMRES results in large improvements. We propose a natural
modification to N-GMRES, which significantly improves the performance in a
testing environment originally used to advocate N-GMRES. Our proposed approach,
which we refer to as O-ACCEL (Objective Acceleration), is novel in that it
minimizes an approximation to the \emph{objective function} on subspaces of
. We prove that O-ACCEL reduces to the Full Orthogonalization
Method for linear systems when the objective is quadratic, which differentiates
our proposed approach from existing acceleration methods. Comparisons with
L-BFGS and N-CG indicate the competitiveness of O-ACCEL. As it can be combined
with domain-specific optimizers, it may also be beneficial in areas where
L-BFGS or N-CG are not suitable.Comment: 18 pages, 6 figures, 5 table
Multigrid Preconditioning for a Space-Time Spectral-Element Discontinuous-Galerkin Solver
In this work we examine a multigrid preconditioning approach in the context of a high- order tensor-product discontinuous-Galerkin spectral-element solver. We couple multigrid ideas together with memory lean and efficient tensor-product preconditioned matrix-free smoothers. Block ILU(0)-preconditioned GMRES smoothers are employed on the coarsest spaces. The performance is evaluated on nonlinear problems arising from unsteady scale- resolving solutions of the Navier-Stokes equations: separated low-Mach unsteady ow over an airfoil from laminar to turbulent ow. A reduction in the number of ne space iterations is observed, which proves the efficiency of the approach in terms of preconditioning the linear systems, however this gain was not reflected in the CPU time. Finally, the preconditioner is successfully applied to problems characterized by stiff source terms such as the set of RANS equations, where the simple tensor product preconditioner fails. Theoretical justification about the findings is reported and future work is outlined
PICPANTHER: A simple, concise implementation of the relativistic moment implicit Particle-in-Cell method
A three-dimensional, parallelized implementation of the electromagnetic
relativistic moment implicit particle-in-cell method in Cartesian geometry
(Noguchi et. al., 2007) is presented. Particular care was taken to keep the
C++11 codebase simple, concise, and approachable. GMRES is used as a field
solver and during the Newton-Krylov iteration of the particle pusher. Drifting
Maxwellian problem setups are available while more complex simulations can be
implemented easily. Several test runs are described and the code's numerical
and computational performance is examined. Weak scaling on the SuperMUC system
is discussed and found suitable for large-scale production runs.Comment: 29 pages, 8 figure
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