Inverse problems arise in a variety of applications: machine learning, image processing, finance, mathematical biology, and more. Solution schemes are formulated by applying algorithms that incorporate regularization techniques and/or statistical approaches. In most cases these solution schemes involve the need to solve large-scale ill-conditioned linear systems that are corrupted by noise and other errors. In this talk we consider new hybrid Krylov subspace methods to solve these linear systems, including how to choose regularization parameters.
Hybrid Iterative Solver for Inverse Problems
Ariana Brown, Emory UniversityAuthors: Ariana Brown, James Nagy, Malena Sabate Landman
2023 AWM Research Symposium
Computational Inverse Problems and Uncertainty Quantification [Organized by Julianne Chung, Rosemary Renaut, and Malena Sabate-Landman]