Sufficient Statistic Memory Approximate Message Passing

• URL: http://arxiv.org/abs/2206.11674v1
• Date: Thu, 23 Jun 2022 13:06:00 GMT
• Title: Sufficient Statistic Memory Approximate Message Passing
• Authors: Lei Liu, Shunqi Huang, and Brian M. Kurkoski
• Abstract summary: A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. This paper proposes a memory AMP (MAMP) under a sufficient statistic condition, named sufficient statistic MAMP (SS-MAMP)
• Score: 5.708490209087275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. However, state evolution does not necessarily guarantee the convergence of iterative algorithms. To solve the convergence problem of AMP-type algorithms in principle, this paper proposes a memory AMP (MAMP) under a sufficient statistic condition, named sufficient statistic MAMP (SS-MAMP). We show that the covariance matrices of SS-MAMP are L-banded and convergent. Given an arbitrary MAMP, we can construct the SS-MAMP by damping, which not only ensures the convergence, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution.

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