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Primal dual methods for Wasserstein gradient flows

Abstract:
Combining the classical theory of optimal transport with modern operator splitting techniques, we develop a new numerical method for nonlinear, nonlocal partial differential equations, arising in models of porous media, materials science, and biological swarming. Our method proceeds as follows: First, we discretize in time, either via the classical JKO scheme or via a novel Crank-Nicolson type method we introduce. Next, we use the Benamou-Brenier dynamical characterization of the Wasserstein distance to reduce computing the solution of the discrete time equations to solving fully discrete minimization problems, with strictly convex objective functions and linear constraints. Third, we compute the minimizers by applying a recently introduced, provably convergent primal dual splitting scheme for three operators [107]. By leveraging the PDEs’ underlying variational structure, our method overcomes stability issues present in previous numerical work built on explicit time discretizations, which suffer due to the equations’ strong nonlinearities and degeneracies. Our method is also naturally positivity and mass preserving and, in the case of the JKO scheme, energy decreasing. We prove that minimizers of the fully discrete problem converge to minimizers of the spatially continuous, discrete time problem as the spatial discretization is refined. We conclude with simulations of nonlinear PDEs and Wasserstein geodesics in one and two dimensions that illustrate the key properties of our approach, including higher order convergence our novel Crank-Nicolson type method, when compared to the classical JKO method.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10208-021-09503-1

Authors


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Institution:
University of Oxford
Department:
MATHEMATICAL INSTITUTE
Sub department:
Mathematical Institute
Oxford college:
Queen's College
Role:
Author
ORCID:
0000-0001-8819-4660


Publisher:
Springer
Journal:
Foundations of Computational Mathematics More from this journal
Volume:
22
Issue:
2022
Pages:
389–443
Publication date:
2021-03-31
Acceptance date:
2021-02-05
DOI:
EISSN:
1615-3383
ISSN:
1615-3375


Language:
English
Keywords:
Pubs id:
1160098
Local pid:
pubs:1160098
Deposit date:
2021-02-05

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