Upper bounds on the growth rates of hard squares and related models via corner transfer matrices
Bibliographic record
Abstract
We study the growth rate of the hard squares lattice gas, equivalent to the number of independent sets on the square lattice, and two related models — non-attacking kings and read-write isolated memory. We use an assortment of techniques from combinatorics, statistical mechanics and linear algebra to prove upper bounds on these growth rates. We start from Calkin and Wilf’s transfer matrix eigenvalue bound, then bound that with the Collatz-Wielandt formula from linear algebra. To obtain an approximate eigenvector, we use an ansatz from Baxter’s corner transfer matrix formalism, optimised with Nishino and Okunishi’s corner transfer matrix renormalisation group method. This results in an upper bound algorithm which no longer requires exponential memory and so is much faster to calculate than a direct evaluation of the Calkin-Wilf bound. Furthermore, it is extremely parallelisable and so allows us to make dramatic improvements to the previous best known upper bounds. In all cases we reduce the gap between upper and lower bounds by 4-6 orders of magnitude. Nous étudions le taux de croissance du système de particules dur sur un réseau carré. Ce taux est équivalent au nombre d’ensembles indépendants sur le réseau carré. Nous étudions également deux modèles qui lui sont reliés : les rois non-attaquants et la mémoire isolée d’écriture-réécriture. Nous utilisons techniques diverses issues de la combinatoire, de la mécanique statistique et de l’algèbre linéaire pour prouver des bornes supérieures sur ces taux de croissances. Nous partons de la borne de Calkin et Wilf sur les valeurs propres des matrices de transfert, que nous bornons à l’aide de la formule de Collatz-Wielandt issue de l’algèbre linéaire. Pour obtenir une valeur approchée d’un vecteur propre, nous utilisons un ansatz du formalisme de Baxter sur les matrices de transfert de coin, que nous optimisons avec la méthode de Nishino et Okunishi qui exploite ces matrices. Il en résulte un algorithme pour calculer la borne supérieure qui n’est plus exponentiel en mémoire et est ainsi beaucoup plus rapide qu’une évaluation directe de la borne de Calkin-Wilf. De plus, cet algorithme est extrêmement parallélisable et permet ainsi une nette amélioration des meilleurs bornes supérieures existantes. Dans tous les cas l’écart entre les bornes supérieures et inférieures s’en trouve réduit de 4 à 6 ordres de grandeur.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".