Description Usage Arguments Value Author(s) References See Also Examples
Set the control parameters for the annealing schedule of spsann functions.
1 2 3  scheduleSPSANN(initial.acceptance = 0.95, initial.temperature = 0.001,
temperature.decrease = 0.95, chains = 500, chain.length = 1,
stopping = 10, x.max, x.min = 0, y.max, y.min = 0, cellsize)

initial.acceptance 
Numeric value between 0 and 1 defining the initial acceptance probability, i.e.
the proportion of proposed system configurations that should be accepted in the first chain. The
optimization is stopped and a warning is issued if this value is not attained. Defaults to

initial.temperature 
Numeric value larger than 0 defining the initial temperature of the system. A
low 
temperature.decrease 
Numeric value between 0 and 1 used as a multiplying factor to decrease the
temperature at the end of each Markov chain. Defaults to 
chains 
Integer value defining the maximum number of chains, i.e. the number of cycles of jitters at
which the temperature and the size of the neighbourhood should be kept constant. Defaults to

chain.length 
Integer value defining the length of each Markov chain relative to the number of
sample points. Defaults to 
stopping 
Integer value defining the maximum allowable number of Markov chains without improvement of
the objective function value. Defaults to 
x.max, x.min, y.max, y.min 
Numeric value defining the minimum and maximum quantity of random noise to
be added to the projected x and ycoordinates. The units are the same as of the projected x and
ycoordinates. If missing, they are estimated from 
cellsize 
Vector with two numeric values defining the horizontal (x) and vertical (y) spacing
between the candidate locations in 
A list with a set of control parameters of the annealing schedule.
Alessandro SamuelRosa alessandrosamuelrosa@gmail.com
Aarts, E. H. L.; Korst, J. H. M. Boltzmann machines for travelling salesman problems. European Journal of Operational Research, v. 39, p. 7995, 1989.
Černý, V. Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, v. 45, p. 4151, 1985.
Brus, D. J.; Heuvelink, G. B. M. Optimization of sample patterns for universal kriging of environmental variables. Geoderma, v. 138, p. 8695, 2007.
Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. Optimization by simulated annealing. Science, v. 220, p. 671680, 1983.
Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E. Equation of state calculations by fast computing machines. The Journal of Chemical Physics, v. 21, p. 10871092, 1953.
van Groenigen, J.W.; Stein, A. Constrained optimization of spatial sampling using continuous simulated annealing. Journal of Environmental Quality. v. 27, p. 10781086, 1998.
Webster, R.; Lark, R. M. Field sampling for environmental science and management. London: Routledge, p. 200, 2013.
optimACDC
, optimCORR
,
optimDIST
, optimMKV
,
optimMSSD
, optimPPL
,
optimSPAN
, optimUSER
.
1  schedule < scheduleSPSANN()

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