A general framework for whiteness-based parameters selection in variational models

• Published in: Computational optimization and applications Vol. 91; no. 2; pp. 457 - 489
• Main Authors: Bevilacqua, Francesca, Lanza, Alessandro, Pragliola, Monica, Sgallari, Fiorella
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2025; Springer Nature B.V
• Subjects: Algorithms; Convex and Discrete Geometry; Inverse problems; Management Science; Mathematics; Mathematics and Statistics; Operations Research; Operations Research/Decision Theory; Optimization; Parameters; Regularization; Regularization methods; Smoothness; Statistics; White noise; Whiteness principle; Bilevel optimization; Variational models; Derivative free methods

In this work, we extend the residual whiteness principle , originally proposed in (Lanza et al. in Electron Trans Numer Anal 53:329–352 2020) for the selection of a single regularization parameter in variational models for inverse problems under additive white noise corruption, to much broader scena...