A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch

• URL: http://arxiv.org/abs/2601.09831v1
• Date: Wed, 14 Jan 2026 19:47:31 GMT
• Title: A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch
• Authors: Guixian Xu, Jinglai Li, Junqi Tang,
• Abstract summary: We provide a new convergence theory for plug-and-play descent (-PGD) under prior where the mismatch is trained on a different gradient distribution to the task data.<n>To the best of our knowledge, this is the first convergence proof -PGD under prior inference.
• Score: 6.546376089353312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we provide a new convergence theory for plug-and-play proximal gradient descent (PnP-PGD) under prior mismatch where the denoiser is trained on a different data distribution to the inference task at hand. To the best of our knowledge, this is the first convergence proof of PnP-PGD under prior mismatch. Compared with the existing theoretical results for PnP algorithms, our new results removed the need for several restrictive and unverifiable assumptions.

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