Primal dual methods for Wasserstein gradient flows
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Publication:2143215
DOI10.1007/s10208-021-09503-1OpenAlexW3140642854MaRDI QIDQ2143215
Li Wang, Katy Craig, Chaozhen Wei, José Antonio Carrillo
Publication date: 31 May 2022
Published in: Foundations of Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1901.08081
optimal transportgradient flowsminimizing movementsoptimization schemesprimal dual methodssteepest descent schemes
Numerical methods involving duality (49M29) Numerical optimization and variational techniques (65K10) Iterative procedures involving nonlinear operators (47J25) Variational methods applied to PDEs (35A15) Continuum models (systems of particles, etc.) arising in equilibrium statistical mechanics (82B21) Nonlinear evolution equations (47J35) Numerical analysis (65-XX)
Related Items (13)
Hessian informed mirror descent ⋮ Neural Parametric Fokker--Planck Equation ⋮ High order spatial discretization for variational time implicit schemes: Wasserstein gradient flows and reaction-diffusion systems ⋮ On the square-root approximation finite volume scheme for nonlinear drift-diffusion equations ⋮ A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling ⋮ Enhanced computation of the proximity operator for perspective functions ⋮ Structure Preserving Primal Dual Methods for Gradient Flows with Nonlinear Mobility Transport Distances ⋮ A first-order computational algorithm for reaction-diffusion type equations via primal-dual hybrid gradient method ⋮ Computation of optimal transport with finite volumes ⋮ A primal-dual approach for solving conservation laws with implicit in time approximations ⋮ Equilibration of Aggregation-Diffusion Equations with Weak Interaction Forces ⋮ Data-driven gradient flows ⋮ A dynamic mass transport method for Poisson-Nernst-Planck equations
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