ConvexRelaxationRungeKutta
swMATH ID: 
34462

Software Authors: 
Ketcheson, David I

Description: 
ConvexRelaxationRungeKutta: Relaxation RungeKutta Methods for Convex Functionals. Relaxation RungeKutta methods are modifications of RungeKutta methods that enforce conservation, dissipation, or other solution properties with respect to any convex functional by the addition of a relaxation parameter that multiplies the RungeKutta update at each time step. Moreover, other desirable stability (such as strong stability preservation) and efficiency (such as low storage requirements) properties are preserved. The technique can be applied to both explicit and implicit RungeKutta methods and requires only a small modification to existing implementations. The computational cost at each step is the solution of one additional scalar algebraic equation for which a good initial guess is available. 
Homepage: 
https://github.com/ranocha/ConvexRelaxationRungeKutta

Source Code: 
https://github.com/ranocha/ConvexRelaxationRungeKutta

Related Software: 
GitHub;
PETSc/TS;
RRK_rr;
BRENT;
SciPy;
minpack;
PETSc

Cited in: 
1 Publication
