5 papers
New flexible and inexact Golub-Kahan algorithms for inverse problems
Malena Sabaté Landman, Silvia Gazzola
This paper introduces a new class of algorithms for solving large-scale linear inverse problems based on new flexible and inexact Golub-Kahan factorizations. The proposed methods i…
Flexible inner-product free Krylov methods for inverse problems
Malena Sabaté Landman
Flexible Krylov methods are a common standpoint for inverse problems. In particular, they are used to address the challenges associated with explicit variational regularization whe…
Randomized flexible Krylov methods for regularization
Malena Sabaté Landman, Yuji Nakatsukasa
The computation of sparse solutions of large-scale linear discrete ill-posed problems remains a computationally demanding task. A powerful framework in this context is the use of i…
Iterative Refinement and Flexible Iteratively Reweighed Solvers for Linear Inverse Problems with Sparse Solutions
Lucas Onisk, Malena Sabaté Landman
This paper presents a new algorithmic framework for computing sparse solutions to large-scale linear discrete ill-posed problems. The approach is motivated by recent perspectives o…
Inner Product Free Krylov Methods for Large-Scale Inverse Problems
Ariana N. Brown, Julianne Chung, James G. Nagy +1
In this study, we introduce two new Krylov subspace methods for solving rectangular large-scale linear inverse problems. The first approach is a modification of the Hessenberg iter…