Imaging geometry through dynamics: the observable representation
| dc.creator | Gaveau, Bernard | |
| dc.creator | Schulman, Lawrence S. | |
| dc.creator | Schulman, Leonard J. | |
| dc.date | 2006-07-17 | |
| dc.date.accessioned | 2026-07-07T08:41:25Z | |
| dc.date.available | 2026-07-07T08:41:25Z | |
| dc.description | For many stochastic processes there is an underlying coordinate space, $V$, with the process moving from point to point in $V$ or on variables (such as spin configurations) defined with respect to $V$. There is a matrix of transition probabilities (whether between points in $V$ or between variables defined on $V$) and we focus on its ``slow'' eigenvectors, those with eigenvalues closest to that of the stationary eigenvector. These eigenvectors are the ``observables,'' and they can be used to recover geometrical features of $V$. | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0607422 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0607422 | |
| dc.identifier | J. Phys. A: Math. Gen. 39 10307-10321 (2006) | |
| dc.identifier | doi:10.1088/0305-4470/39/33/004 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/141670 | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Other Condensed Matter | |
| dc.title | Imaging geometry through dynamics: the observable representation | |
| dc.type | text |