simulated_annealing_module Module

number of variables in the function to be optimized.

denotes whether the function should be maximized or minimized. a true value denotes maximization while a false value denotes minimization. intermediate output (see iprint) takes this into account.

error tolerance for termination. if the final function values from the last neps temperatures differ from the corresponding value at the current temperature by less than eps and the final function value at the current temperature differs from the current optimal function value by less than eps, execution terminates and ier = 0 is returned.

number of cycles. after ns function evaluations, each element of vm is adjusted according to the input step_mode the suggested value is 20.

number of iterations before temperature reduction. after ntns function evaluations, temperature (t) is changed by the factor rt. value suggested by corana et al. is max(100, 5n). see goffe et al. for further advice.

number of final function values used to decide upon termination. see eps. suggested value is 4.

the maximum number of function evaluations. if it is exceeded, ier = 1.

if false, the initial guess is ignored and a random point in the bounds is used for the first function evaluation

number of times to run the main loop (must be >=1)

the lower bound for the allowable solution variables.

the upper bound for the allowable solution variables. if the algorithm chooses x(i) < lb(i) or x(i) > ub(i), i = 1, n, a point is from inside is randomly selected. this this focuses the algorithm on the region inside ub and lb. unless the user wishes to concentrate the search to a particular region, ub and lb should be set to very large positive and negative values, respectively. note that the starting vector x should be inside this region. also note that lb and ub are fixed in position, while vm is centered on the last accepted trial set of variables that optimizes the function.

vector that controls the step length adjustment. the suggested value for all elements is 2.0.

controls printing inside sa:

the first seed for the random number generator.

the second seed for the random number generator. different values for iseed1 and iseed2 will lead to an entirely different sequence of trial points and decisions on downhill moves (when maximizing). see goffe et al. on how this can be used to test the results of sa.

how to vary vm after ns cycles.

for step_mode=3, the factor to adjust vm

unit number for prints.

if the optional f value is known, it can be specified by optimal_f.

if optimal_f_specified=True the solver will stop if this value is achieved.

absolute tolerance for the optimal_f check

the user's function

lower bound

upper bound

if the optional f value is known, it can be specified by optimal_f. [Default is False]

if optimal_f_specified=True the solver will stop if this value is achieved.

absolute tolerance for the optimal_f check

on input: the starting values for the variables of the function to be optimized. [Will be replaced by final point]

the temperature reduction factor. the value suggested by corana et al. is .85. see goffe et al. for more advice.

on input, the initial temperature. see goffe et al. for advice. on output, the final temperature. Note that if t=0, then all downhill steps will be rejected

the step length vector. on input it should encompass the region of interest given the starting value x. for point x(i), the next trial point is selected is from x(i) - vm(i) to x(i) + vm(i). since vm is adjusted so that about half of all points are accepted, the input value is not very important (i.e. is the value is off, sa adjusts vm to the correct value).

the variables that optimize the function.

the optimal value of the function.

the number of accepted function evaluations.

the total number of function evaluations. in a minor point, note that the first evaluation is not used in the core of the algorithm; it simply initializes the algorithm.

the error return number:

input optimization variable vector to perturb

step length vector

the perturbed x value

the value of the user function at xp

total number of function evaluations

status output code

to use the input x the first time

the first seed for the random number generator.

the second seed for the random number generator.