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Continuum limits of nonlocal \(p\)-Laplacian variational problems on graphs. (English) Zbl 1447.65056

The authors study numerical approximations of the nonlocal variational problem consisting of minimizing in \(L_2\) the sum of quadratic data fidelity and a regularization term corresponding to the \(L_p\)-norm of the nonlocal gradient. Discrete versions of continuum models based on nonlocal regularization are used frequently in the signal, image, data processing, machine learning, and computer vision. General error estimates in the \(L_2\) norm are given for the error between the continuum extension of the numerical solution to the discrete variational problem and its continuum analogue. Convergence rates are obtained under very mild conditions on the kernel and the initial data.
These results are applied, using the theory graph limits, to dynamical networks on simple and weighted dense graphs, showing that the minimizers of the sequence of discrete problems converge to that of the continuum problem. The authors also study networks on random inhomogeneous graphs, building upon the error estimates. The variational regularization problem is applied to point cloud denoising and to signal denoising problems and the error bounds are illustrated numerically.

MSC:

65M12 Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs
65M15 Error bounds for initial value and initial-boundary value problems involving PDEs
41A17 Inequalities in approximation (Bernstein, Jackson, Nikol’skiĭ-type inequalities)
05C80 Random graphs (graph-theoretic aspects)
05C90 Applications of graph theory
49M25 Discrete approximations in optimal control
65K15 Numerical methods for variational inequalities and related problems
35A15 Variational methods applied to PDEs
35Q35 PDEs in connection with fluid mechanics
35K20 Initial-boundary value problems for second-order parabolic equations
35R02 PDEs on graphs and networks (ramified or polygonal spaces)
35R09 Integro-partial differential equations

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