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| author | kurchan.jorge <kurchan.jorge@gmail.com> | 2022-02-16 15:12:26 +0000 |
|---|---|---|
| committer | node <node@git-bridge-prod-0> | 2022-02-16 16:12:33 +0000 |
| commit | 1477036fd7c2f741bc6be5603eed5e6d498c7065 (patch) | |
| tree | 8eb3adae042a43371c8281a36b2a69dd86069be0 | |
| parent | 557887f782cf28547e52f2212b4b18ea3501efc6 (diff) | |
| download | JPA_55_434006-1477036fd7c2f741bc6be5603eed5e6d498c7065.tar.gz JPA_55_434006-1477036fd7c2f741bc6be5603eed5e6d498c7065.tar.bz2 JPA_55_434006-1477036fd7c2f741bc6be5603eed5e6d498c7065.zip | |
Update on Overleaf.
| -rw-r--r-- | stokes.tex | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -66,7 +66,7 @@ study the average of an ensemble. The paradigm of this is the spin-glass, where ground state energy {\em on average over randomness}. Spin glass theory gave a surprise: random landscapes come in two kinds: those that -have a `threshold level' of energy, below which there are many minima but no saddles, +have a `threshold level' of energy, below which there are many minima but almost no saddles, separated by high barriers, and those that have all sorts of saddles all the way down to the lowest energy levels, and local minima are separated by relatively small barriers. The latter are still complex, but good solutions are easier to find. |