Sufficient Statistic Memory Approximate Message Passing
| Autor: | Lei Liu, Shunqi Huang, Brian M. Kurkoski |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Information Theory (cs.IT) Computer Science - Information Theory FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Mathematics - Statistics Theory Statistics Theory (math.ST) Electrical Engineering and Systems Science - Signal Processing Machine Learning (cs.LG) |
| Zdroj: | 2022 IEEE International Symposium on Information Theory (ISIT). |
| DOI: | 10.1109/isit50566.2022.9834568 |
| Popis: | 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. Accepted by the 2022 IEEE International Symposium on Information Theory (ISIT). arXiv admin note: substantial text overlap with arXiv:2112.15327 |
| Databáze: | OpenAIRE |
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