Numerical Methods for Partial Differential Equations · 2018 · 68 citations · 40 references
ComputingComputational EngineeringComputational PhysicsSlope StabilityThin FilmsConvergence AnalysisFinite Difference MethodsGradient-based OptimizationNumerical ConvergenceNumerical Methods For Differential EquationsTime IntegrationBackward Difference SchemesBackward Differentiation FormulasEnergy StabilityEpitaxial Thin FilmsFinite Difference SchemesNonlinear Conjugate GradientStability Of Finite Difference SchemesSteepest Descent
In this article, we study a new second‐order energy stable Backward Differentiation Formula (BDF) finite difference scheme for the epitaxial thin film equation with slope selection (SS). One major challenge for higher‐order‐in‐time temporal discretizations is how to ensure an unconditional energy stability without compromising numerical efficiency or accuracy. We propose a framework for designing a second‐order numerical scheme with unconditional energy stability using the BDF method with constant coefficient stabilizing terms. Based on the unconditional energy stability property that we establish, we derive an stability for the numerical solution and provide an optimal convergence analysis. To deal with the highly nonlinear four‐Laplacian term at each time step, we apply efficient preconditioned steepest descent and preconditioned nonlinear conjugate gradient algorithms to solve the corresponding nonlinear system. Various numerical simulations are presented to demonstrate the stability and efficiency of the proposed schemes and solvers. Comparisons with other second‐order schemes are presented.
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Function minimization by conjugate gradients
R. Fletcher · The Computer Journal · 1964 · 4.9K citations · Full text