19 citations · 75 across the 18 of their papers we have counts for
6 papers · 1 filter
Numerical analysis of a first-order computational algorithm for reaction-diffusion equations via the primal-dual hybrid gradient method
Shu Liu, Xinzhe Zuo, Stanley Osher +1
In arXiv:2305.03945 [math.NA], a first-order optimization algorithm has been introduced to solve time-implicit schemes of reaction-diffusion equations. In this research, we conduct…
Primal-dual hybrid gradient algorithms for computing time-implicit Hamilton-Jacobi equations
Tingwei Meng, Wenbo Hao, Siting Liu +2
Hamilton-Jacobi (HJ) partial differential equations (PDEs) have diverse applications spanning physics, optimal control, game theory, and imaging sciences. This research introduces…
A first-order computational algorithm for reaction-diffusion type equations via primal-dual hybrid gradient method
Shu Liu, Siting Liu, Stanley Osher +1
We propose an easy-to-implement iterative method for resolving the implicit (or semi-implicit) schemes arising in solving reaction-diffusion (RD) type equations. We formulate the n…
High order spatial discretization for variational time implicit schemes: Wasserstein gradient flows and reaction-diffusion systems
Guosheng Fu, Stanley Osher, Wuchen Li
We design and compute first-order implicit-in-time variational schemes with high-order spatial discretization for initial value gradient flows in generalized optimal transport metr…
High order computation of optimal transport, mean field planning, and mean field games
Guosheng Fu, Siting Liu, Stanley Osher +1
Mean-field games (MFGs) have shown strong modeling capabilities for large systems in various fields, driving growth in computational methods for mean-field game problems. However,…
A primal-dual approach for solving conservation laws with implicit in time approximations
Siting Liu, Stanley Osher, Wuchen Li +1
In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an…