Pith. sign in

REVIEW 2 cited by

Unifying AMP Algorithms for Rotationally-Invariant Models

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.01574 v1 pith:DU7YBRLD submitted 2024-12-02 math.ST cs.ITcs.LGmath.ITmath.PRstat.TH

Unifying AMP Algorithms for Rotationally-Invariant Models

classification math.ST cs.ITcs.LGmath.ITmath.PRstat.TH
keywords algorithmsmodelsalgorithmcumulantsframeworkfreerotationally-invariantallows
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

This paper presents a unified framework for constructing Approximate Message Passing (AMP) algorithms for rotationally-invariant models. By employing a general iterative algorithm template and reducing it to long-memory Orthogonal AMP (OAMP), we systematically derive the correct Onsager terms of AMP algorithms. This approach allows us to rederive an AMP algorithm introduced by Fan and Opper et al., while shedding new light on the role of free cumulants of the spectral law. The free cumulants arise naturally from a recursive centering operation, potentially of independent interest beyond the scope of AMP. To illustrate the flexibility of our framework, we introduce two novel AMP variants and apply them to estimation in spiked models.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Gaussian-Process Dynamics of Diagonal Expectation Propagation under Variance-Profile Gaussian Measurements

    eess.SP 2026-06 unverdicted novelty 7.0

    Diagonal EP under variance-profile Gaussian matrices produces Gaussian-process dynamics with profile-dependent memory instead of conventional scalar state evolution.

  2. Asymptotic Analysis of Nonlinear One-Bit Precoding in Massive MIMO Systems via Approximate Message Passing

    cs.IT 2025-09 unverdicted novelty 7.0

    The paper derives a closed-form symbol error probability for convex-relaxation-then-quantization one-bit precoding in the large-system limit using an auxiliary AMP iteration that incorporates the quantization nonlinearity.