A regularized method for quadratic optimization problems with finite-dimensional degeneracy
C. G. Gebhardt, I. Romero

TL;DR
This paper introduces a perturbative regularization and finite element discretization method for quadratic optimization problems with finite-dimensional degeneracy, ensuring convergence of solutions and extending previous approaches for Neumann problems.
Contribution
It generalizes a regularization approach for quadratic optimization with degeneracy, providing a convergent finite element approximation framework.
Findings
Families of functionals $ ext{Gamma}$-converge to the original problem
Minimizers of regularized problems converge to the true solution
Method offers a sparsity-preserving, numerically efficient alternative
Abstract
We propose and analyze a perturbative regularization method to approximate quadratic optimization problems with finite-dimensional degeneracy. The original problem is first approximated by a regularized problem depending on a small positive parameter, and then discretized using the finite element method. The resulting families of continuous and discrete functionals -converge to the functional of the original problem and the corresponding minimizers converge as well. Our method generalizes the approach proposed in Kaleem et al. (2026) for numerically approximating pure Neumann problems, which represents the cornerstone of a sparsity-preserving, numerically efficient alternative to the methods developed in Bochev and Lehoucq (2005), Ivanov et al. (2019) and Roccia et al. (2024). References: A. Kaleem, C. Gebhardt, and I. Romero. On the pure traction problem of linear elasticity: a…
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Taxonomy
TopicsAdvanced Numerical Methods in Computational Mathematics · Numerical methods in inverse problems · Topology Optimization in Engineering
