12 citations · 13 across the 5 of their papers we have counts for
5 papers
On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime
Shuai Jiang, Alexey Voronin, Eric Cyr +1
Spectral bias, the tendency of neural networks to learn low frequencies first, can be both a blessing and a curse. While it enhances the generalization capabilities by suppressing…
Generalized Optimal AMG Convergence Theory for Stokes Equations Using Smooth Aggregation and Vanka Relaxation Strategies
Ahsan Ali, James J. Brannick, Karsten Kahl +4
This paper discusses our recent generalized optimal algebraic multigrid (AMG) convergence theory applied to the steady-state Stokes equations discretized using Taylor-Hood elements…
Monolithic Multigrid Preconditioners for High-Order Discretizations of Stokes Equations
Alexey Voronin, Graham Harper, Scott MacLachlan +2
This work introduces and assesses the efficiency of a monolithic MG multigrid framework designed for high-order discretizations of stationary Stokes systems using Taylor-Hood a…
Monolithic Algebraic Multigrid Preconditioners for the Stokes Equations
Alexey Voronin, Scott MacLachlan, Luke N. Olson +1
We investigate a novel monolithic algebraic multigrid (AMG) preconditioner for the Taylor-Hood () and Scott-Vogelius ($\pmb{\mathbb{P}}_2/\mathbb{P…
Low-order preconditioning of the Stokes equations
Alexey Voronin, Yunhui He, Scott MacLachlan +2
A well-known strategy for building effective preconditioners for higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for…