Preconditioned accelerated gradient descent methods for locally Lipschitz smooth objectives with applications to the solution of nonlinear PDEs

DOI10.1007/s10915-021-01615-8OpenAlexW3197024903WikidataQ114225589 ScholiaQ114225589MaRDI QIDQ1983570

Abner J. Salgado, Jea-Hyun Park, Steven M. Wise

Publication date: 10 September 2021

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.06732

zbMATH Keywords

convex optimizationpreconditioningnonlinear elliptic partial differential equationspseudo-spectral methodsLyapunovmomentum methodNesterov acceleration

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Numerical solutions to abstract evolution equations (65J08) Acceleration of convergence in numerical analysis (65B99)

Related Items (2)

Benchmark Computations of the Phase Field Crystal and Functionalized Cahn-Hilliard Equations via Fully Implicit, Nesterov Accelerated Schemes ⋮ Nesterov's Method for Convex Optimization

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