Real-space renormalization-group methods for hierarchical spin glasses
Michele Castellana

TL;DR
This paper compares two real-space RG methods for hierarchical spin glasses, revealing limitations of the ensemble RG approach in predicting critical exponents below the upper critical dimension and highlighting the ongoing challenge of developing accurate RG methods for disordered systems.
Contribution
The paper clarifies the reasons for discrepancies between sample-by-sample and ensemble RG methods and demonstrates that the ensemble RG predicts a marginally stable fixed point, which does not match known critical exponents.
Findings
Sample-by-sample RG yields correct mean-field critical exponent above critical dimension.
Ensemble RG predicts a fixed point inconsistent with known critical exponents.
Finding an accurate real-space RG method for spin glasses below the upper critical dimension remains an open problem.
Abstract
We focus on two real-space renormalization-group (RG) methods recently proposed for a hierarchical model of a spin glass: A sample-by-sample method, in which the RG transformation is performed separately on each disorder sample, and an ensemble RG (ERG) method [M. C. Angelini, G. Parisi, and F. Ricci-Tersenghi. Ensemble renormalization group for disordered systems. , 87(13):134201, 2013] in which the transformation is based on an average over samples. Above the upper critical dimension, the sample-by-sample method yields the correct mean-field value for the critical exponent related to the divergence of the correlation length, while it does not predict the correct qualitative behavior of below the upper critical dimension. On the other hand, the ERG procedure has been claimed to predict the correct behavior of both above and below the upper…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
