Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging

ArXi:2602.03855v2 Announce Type: replace-cross Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based approaches such as deep unrolling and meta-learning achieve strong empirical performance, they typically lack explicit control over descent and curvature, limiting robustness. We propose a learned Majorization-Minimization (MM) framework for inverse problems within a bilevel optimization setting.