Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing

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Authors: Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
Cited by: 0
References: 23

Abstract

Abstract We consider sparse signal reconstruction via minimization of the smoothly clipped absolute deviation penalty, and develop one-step replica-symmetry-breaking (1RSB) extensions of approximate message passing (AMP), termed 1RSB-AMP. Starting from the 1RSB formulation of belief propagation, we derive explicit update rules of 1RSB-AMP together with the corresponding state evolution (1RSB-SE) equations. A detailed comparison shows that 1RSB-AMP and 1RSB-SE agree remarkably well at the macroscopic level, even in parameter regions where replica-symmetric (RS) AMP, termed RS-AMP, diverges and where the 1RSB description itself is not expected to be thermodynamically exact. Fixed-point analysis of 1RSB-SE reveals a phase diagram consisting of success, failure, and diverging phases, as in the RS case. However, the diverging-region boundary now depends on the Parisi parameter due to the 1RSB ansatz, and we propose a new criterion—minimizing the size of the diverging region—rather than the conventional zero-complexity condition, to determine its value. Combining this criterion with the nonconvexity-control protocol proposed in a previous RS study improves the algorithmic limit of perfect reconstruction compared with RS-AMP. Numerical solutions of 1RSB-SE and experiments with 1RSB-AMP confirm that this improved limit is achieved in practice, though the gain is modest and remains slightly inferior to the Bayes-optimal threshold. We also report the behavior of thermodynamic quantities—overlaps, free entropy, complexity, and the non-self-averaging susceptibility—that characterize the 1RSB phase in this problem.

Fields of Study & Subjects

Sparse and Compressive Sensing Techniques Blind Source Separation Techniques Functional Brain Connectivity Studies

Publication details

Journal / source
Journal of Statistical Mechanics Theory and Experiment
Volume
2026
Issue
9
Pages
093403–093403
Publication date
1 September 2026