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3.4.1 Simulated Annealing
Consider a Metropolis walk through the space of ``states''
with
where
is a ``cost function'' to be
minimized, and a tunable parameter (a reciprocal
``temperature''.)
 Low
smaller variation of ;
higher
are accessible
 High
will tend to go ``downhill''
EXERCISE:
Create (fake!) a table of ``measured values with errors'' according to

(3.2) 
with coming from a Gauss distribution with suitable variance, and
with the function defined by

(3.3) 
(
being a set of arbitrary coefficients).
Using these data, try to reconstruct the parameters
by fitting the theoretical function to the table points
. The cost function is

(3.4) 
Choose an initial vector
and perform an MC random walk
through
space, slowly lowering the temperature.
Next: 3.4.2 Genetic Algorithms
Up: 3.4 Stochastic Optimization
Previous: 3.4 Stochastic Optimization
Franz J. Vesely Oct 2005
See also: "Computational Physics  An Introduction," KluwerPlenum 2001