SciML / OrdinaryDiffEq.jl

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
https://diffeq.sciml.ai/latest/
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Diagonal matrices in the caching ExpRK methods. #409

Closed MSeeker1340 closed 6 years ago

MSeeker1340 commented 6 years ago

A special case for the caching ExpRK methods is when the linear operator is diagonal. For example, this occurs when you have Fourier decompositions of a PDE operator with periodic BCs. The current implementation of the integrators treat all matrices in a generic way, and it would help to have special treatment for the diagonal case.

MSeeker1340 commented 6 years ago

Closed via https://github.com/JuliaDiffEq/ExponentialUtilities.jl/releases/tag/v1.1.0