Convergence analysis of equilibrium methods for inverse problems

Solving inverse problems $Ax = y$ is central to a variety of practically important fields such as medical imaging, remote sensing, and non-destructive testing. The most successful and theoretically best-understood method is convex variational regularization, where approximate but stable solutions are defined as minimizers of $\|A(\cdot) - y^\delta\|^2 / 2 + \alpha \mathcal{R}(\cdot)$, with $\mathcal{R}$ a regularization functional. Recent methods such as deep equilibrium models and plug-and-play approaches, however, go beyond variational regularization. Motivated by these innovations, we introduce implicit non-variational (INV) regularization, where approximate solutions are defined as solutions of $A^*(A x - y^\delta) + \alpha R(x) = 0$ for some regularization operator $R$. When the regularization operator is the gradient of a functional, INV reduces to classical variational regularization. However, in methods like DEQ and PnP, $R$ is not a gradient field, and the existing theoretical foundation remains incomplete. To address this, we establish stability and convergence results in this broader setting, including convergence rates and stability estimates measured via a absolute Bregman distance.