Árbore:
331a473f1a
11.0
12.0
12.1
12.2
13.0
13.1
13.37
14.0
14.1
14.2
15.0
bedlam
bedlam-name-fixes
dive
erik
gitignore
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master
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python-renames
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user/anddt/misc/discount
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user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
user/anddt/system/postgresql
user/anddt/system/testdisk
user/anddt/system/worker
user/anddt/system/xen
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user/chris.willing/updates
user/dslackw/updates
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willysr
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| README | ce47b49a3c academic/dwave-neal: Added (a simulated annealing sampler) | %!s(int64=2) %!d(string=hai) anos |
| dwave-neal.SlackBuild | ce47b49a3c academic/dwave-neal: Added (a simulated annealing sampler) | %!s(int64=2) %!d(string=hai) anos |
| dwave-neal.info | ce47b49a3c academic/dwave-neal: Added (a simulated annealing sampler) | %!s(int64=2) %!d(string=hai) anos |
| slack-desc | ce47b49a3c academic/dwave-neal: Added (a simulated annealing sampler) | %!s(int64=2) %!d(string=hai) anos |
README
An implementation of a simulated annealing sampler.
A simulated annealing sampler can be used for approximate Boltzmann
sampling or heuristic optimization. This implementation approaches
the equilibrium distribution by performing updates at a sequence of
increasing beta values, beta_schedule, terminating at the target
beta. Each spin is updated once in a fixed order per point in the
beta_schedule according to a Metropolis- Hastings update. When beta
is large the target distribution concentrates, at equilibrium,
over ground states of the model. Samples are guaranteed to match
the equilibrium for long 'smooth' beta schedules.
A simulated annealing sampler can be used for approximate Boltzmann
sampling or heuristic optimization. This implementation approaches
the equilibrium distribution by performing updates at a sequence of
increasing beta values, beta_schedule, terminating at the target
beta. Each spin is updated once in a fixed order per point in the
beta_schedule according to a Metropolis- Hastings update. When beta
is large the target distribution concentrates, at equilibrium,
over ground states of the model. Samples are guaranteed to match
the equilibrium for long 'smooth' beta schedules.