Physics-Aware Linearized ADMM and Its Unrolling
Recently, partial differential equations (PDEs) have been used to directly model the measurement process in signal processing, although their evaluation is costly. In this paper, we propose a novel alternating direction method of multipliers (ADMM)-based algorithm called physics-aware linearized ADMM (PA-LADMM) for inverse problems from PDE-based measurement processes. The key idea is the linearization of the subproblem with PDEs, leading to a cost-efficient update rule that calls only a PDE solver and its gradient evaluation per iteration. The algorithm has a theoretical convergence guarantee under certain conditions. In addition, we combine it with deep unfolding to unroll the PA-LADMM ...
| Authors | Satoshi Takabe, Shunta Arai, Tadashi Wadayama |
|---|---|
| Primary category | eess.SP |
| Published | 1 June 2026 |
| Last updated at source | 9 September 2026 |
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