161 lines
6.4 KiB
Matlab
161 lines
6.4 KiB
Matlab
% ASAMIN A gateway function to Adaptive Simulated Annealing (ASA)
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%
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% ASAMIN is a matlab gateway function to Lester Ingber's Adaptive
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% Simulated Annealing (ASA)
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%
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% Copyright (c) 1999-2001 Shinichi Sakata. All Rights Reserved.
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%
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% $Id: asamin.m,v 1.1.2.1 2004/03/27 14:41:06 michel Exp $
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%
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% Usage:
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%
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% asamin ('set')
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%
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% lists the current value of each option.
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%
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% asamin ('set', opt_name)
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%
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% shows the current value of the option given by a character string
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% opt_name; e.g.,
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%
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% asamin ('set', 'seed')
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%
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% asamin ('set', opt_name, opt_value)
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%
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% set the value opt_value to the option opt_name; e.g.,
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%
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% asamin ('set', 'seed', 654342)
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% asamin ('set', 'asa_out_file', 'example.log')
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%
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% The valid options in these commands are:
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%
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% rand_seed
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% test_in_cost_func
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% use_rejected_cost
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% asa_out_file
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% limit_acceptances
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% limit_generated
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% limit_invalid
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% accepted_to_generated_ratio
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% cost_precision
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% maximum_cost_repeat
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% number_cost_samples
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% temperature_ratio_scale
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% cost_parameter_scale
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% temperature_anneal_scale
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% include_integer_parameters
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% user_initial_parameters
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% sequential_parameters
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% initial_parameter_temperature
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% acceptance_frequency_modulus
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% generated_frequency_modulus
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% reanneal_cost
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% reanneal_parameters
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% delta_x
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%
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% rand_seed is the seed of the random number generation in ASA.
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%
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% If test_in_cost_func is set to zero, the cost function should
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% simply return the value of the objective function. When
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% test_in_cost_func is set to one, asamin () calls the cost
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% function with a threshold value as well as the parameter
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% value. The cost function needs to judge if the value of the cost
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% function exceeds the threshold as well as compute the value of
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% the cost function when asamin () requires. (See COST FUNCTION
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% below for details.)
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%
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% All other items but use_rejected_cost belong to structure
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% USER_OPTIONS in ASA. See ASA_README in the ASA package for
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% details. The default value of use_rejected_cost is zero. If you
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% set this option to one, ASA uses the current cost value to
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% compute certain indices, even if the current state is rejected by
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% the user cost function, provided that the current cost value is
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% lower than the cost value of the past best state. (See COST
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% FUNCTION below about the user cost function.)
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%
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% asamin ('reset')
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% resets all option values to the hard-coded default values.
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%
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% [fstar,xstar,grad,hessian,state] = ...
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% asamin ('minimize', func, xinit, xmin, xmax, xtype,...
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% parm1, parm2, ...)
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%
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% minimizes the cost function func (also see COST FUNCTION below).
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% The argument xinit specifies the initial value of the arguments
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% of the cost function. Each element of the vectors xmin and xmax
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% specify the lower and upper bounds of the corresponding
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% argument. The vector xtype indicates the types of the
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% arguments. If xtype(i) is -1 if the i'th argument is real;
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% xtype(i) is 1 if the i'th argument is integer. If this argument
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% should be ignored in reannealing, multiply the corresponding
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% element of xtype by 2 so that the element is 2 or -2. All
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% parameters following xtype are optional and simply passed to the
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% cost function each time the cost function is called.
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%
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% This way of calling asamin returns the following values:
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%
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% fstar
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% The value of the objective function at xstar.
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% xstar
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% The argument vector at the exit from the ASA routine. If things go
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% well, xstar should be the minimizer of "func".
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% grad
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% The gradient of "func" at xstar.
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% hessian
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% The Hessian of "func" at xstar.
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% state
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% The vector containing the information on the exit state.
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% state(1) is the exit_code, and state(2) is the cost flag. See
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% ASA_README for details.
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%
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%
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%
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% COST FUNCTION
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%
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% If test_in_cost_func is set to zero, asamin () calls the "cost
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% function" (say, cost_func) with one argument, say x (the real cost
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% function is evaluated at this point). Cost_func is expected to
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% return the value of the objective function and cost_flag, the
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% latter of which must be zero if any constraint (if any) is
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% violated; otherwise one.
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%
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% When test_in_cost_func is equal to one, asamin () calls the "cost
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% function" (say, cost_func) with three arguments, say, x (at which
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% the real cost function is evaluated), critical_cost_value, and
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% no_test_flag. Asamin expects cost_func to return three scalar
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% values, say, cost_value, cost_flag, and user_acceptance_flag in the
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% following manner.
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%
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% 1. The function cost_func first checks if x satisfies the
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% constraints of the minimization problem. If any of the
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% constraints is not satisfied, cost_func sets zero to cost_flag
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% and return. (user_acceptance_flag and cost_value will not be used
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% by asamin () in this case.) If all constraints are satisfied, set
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% one to cost_flag, and proceed to the next step.
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%
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% 2. If asamin () calls cost_func with no_test_flag==1, cost_func
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% must compute the value of the cost function, set it to cost_value
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% and return. When no_test_flag==0, cost_func is expected to judge
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% if the value of the cost function is greater than
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% critical_cost_value. If the value of the cost function is found
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% greater than critical_cost_value, cost_func must set zero to
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% user_acceptance_flag and return. (asamin () will not use
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% cost_value in this case.) On the other hand, if the value of the
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% cost function is found no greater than critical_cost_value,
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% cost_func must compute the cost function at x, set it to
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% cost_value, and set one to user_acceptance_flag.
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%
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% Remark: To understand the usefulness of test_in_cost_func == 1,
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% note that it is sometimes easier to check if the value of the cost
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% function is greater than critical_cost_value than compute the value
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% of the cost function. For example, suppose that the cost function g
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% is implicitly defined by an equation f(g(x),x)=0, where f is
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% strictly increasing in the first argument, and evaluation of g(x)
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% is computationally expensive (e.g., requiring an iterative method
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% to find a solution to f(y,x)=0). But we can easily show that
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% f(critical_cost_value,x) < 0 if and only if g(x) >
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% critical_cost_value. We can judge if g(x) > critical_cost_value by
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% computing f(critical_cost_value,x). The value of g(x) is not
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% necessary.
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%
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