A Proximal Primal-Dual Approach to Generalized JKO Schemes for Doubly Nonlinear Parabolic Equations
This work provides an incremental improvement in computational methods for solving doubly nonlinear parabolic equations, primarily benefiting researchers in applied mathematics and numerical analysis.
The authors tackled the numerical solution of a broad class of nonlinear partial differential equations, including the p-Laplace and relativistic heat equations, by developing a proximal primal-dual method for generalized JKO schemes, resulting in efficient numerical approximations validated through qualitative recovery of known cases.
Variational methods based on optimization strategies are proposed to numerically solve a large family of nonlinear partial differential equations. They are all particular instances of gradient flows with general costs, including the $p$-Laplace equation and flux-limited equations such as the relativistic heat equation. This is achieved by computing explicit formulas for proximal operators with general costs amenable to efficient numerical approximation. We showcase our numerical approach via validation of the results by recovering the qualitative behavior of particular known cases of this large family of steepest descent evolutions.