Preconditioned Steepest Descent Methods for some Nonlinear Elliptic Equations Involving p-Laplacian Terms
Wenqiang Feng, Abner J. Salgado, Cheng Wang, Steven M. Wise

TL;DR
This paper develops and analyzes preconditioned steepest descent methods for solving complex nonlinear elliptic equations involving p-Laplacian terms, providing convergence guarantees and demonstrating efficiency through numerical experiments in physical models.
Contribution
It introduces a general framework for PSD methods with preconditioning for nonlinear elliptic equations, including new convergence results and applications to high-order problems.
Findings
Proves geometric convergence of the PSD scheme under certain conditions.
Validates the approach with numerical simulations on physical models.
Provides sharper convergence results for p-Laplacian systems.
Abstract
We describe and analyze preconditioned steepest descent (PSD) solvers for fourth and sixth-order nonlinear elliptic equations that include p-Laplacian terms on periodic domains in 2 and 3 dimensions. The highest and lowest order terms of the equations are constant-coefficient, positive linear operators, which suggests a natural preconditioning strategy. Such nonlinear elliptic equations often arise from time discretization of parabolic equations that model various biological and physical phenomena, in particular, liquid crystals, thin film epitaxial growth and phase transformations. The analyses of the schemes involve the characterization of the strictly convex energies associated with the equations. We first give a general framework for PSD in generic Hilbert spaces. Based on certain reasonable assumptions of the linear pre-conditioner, a geometric convergence rate is shown for the…
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