From the 1 of 5 linked papers with an AI index.
5 papers
Mesh-dependent iteration count growth in primal-dual active set strategies
Ioannis P. A. Papadopoulos, Michael Hintermüller, Michael Hintermüller
The paper investigates how the number of iterations required by primal‑dual active set methods grows as the mesh is refined for obstacle and Signorini problems, showing linear grow…
LeAP-SSN: A Semismooth Newton Method with Global Convergence Rates
Amal Alphonse, Pavel Dvurechensky, Ioannis P. A. Papadopoulos +1
We propose LeAP-SSN (Levenberg--Marquardt Adaptive Proximal Semismooth Newton method), a semismooth Newton-type method with a simple, parameter-free globalisation strategy that gua…
The latent variable proximal point algorithm for variational problems with inequality constraints
Jørgen S. Dokken, Patrick E. Farrell, Brendan Keith +2
The latent variable proximal point (LVPP) algorithm is a framework for solving infinite-dimensional variational problems with pointwise inequality constraints. The algorithm is a s…
Hierarchical proximal Galerkin: a fast -FEM solver for variational problems with pointwise inequality constraints
Ioannis P. A. Papadopoulos
We leverage the proximal Galerkin algorithm (Keith and Surowiec, Foundations of Computational Mathematics, 2024), a recently introduced mesh-independent algorithm, to obtain a high…
A Globalized Inexact Semismooth Newton Method for Nonsmooth Fixed-point Equations involving Variational Inequalities
Amal Alphonse, Constantin Christof, Michael Hintermüller +1
We develop a semismooth Newton framework for the numerical solution of fixed-point equations that are posed in Banach spaces. The framework is motivated by applications in the fiel…