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math.OC2024
A sparse optimization approach to infinite infimal convolution regularization
Kristian Bredies, Marcello Carioni, Martin Holler +2
In this paper we introduce the class of infinite infimal convolution functionals and apply these functionals to the regularization of ill-posed inverse problems. The proposed regul…
math.OC2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee +1
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced p…
math.OC2024
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize (L2O) is an emerging research area in large-scale optimization with applications in data science. Recently, researchers have proposed a novel L2O framework cal…