3 papers
cs.LG2026
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…
math.NA2025
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…
math.NA2024
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…