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Hierarchical proximal Galerkin: a fast hp-FEM solver for variational problems with pointwise inequality constraints

18 Dec 2024arXiv:2412.13733links table onlyarchive 2025-07-28

Ioannis P. A. Papadopoulos

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We leverage the proximal Galerkin algorithm (Keith and Surowiec, Foundations of Computational Mathematics, 2024), a recently introduced mesh-independent algorithm, to obtain a high-order finite element solver for variational problems, posed on tensor-product domains, with pointwise inequality constraints. This is achieved by discretizing the saddle point systems, arising from the latent variable proximal point method, with the hierarchical p-finite element basis. This results in discretized sparse Newton systems that admit a simple and effective block preconditioner. The solver can handle both obstacle-type, u ≤φ, and gradient-type, |∇u| ≤φ, constraints. We apply the resulting algorithm to solve obstacle problems with hp-adaptivity, a three-dimensional obstacle problem, a gradient-type constrained problem, and the thermoforming problem, an example of an obstacle-type quasi-variational inequality. We observe hp-robustness in the number of Newton iterations and only mild growth in the number of inner Krylov iterations to solve the Newton systems. Crucially we also provide wall-clock timings that are faster than low-order discretization counterparts.

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