334 lines
14 KiB
Matlab
334 lines
14 KiB
Matlab
function [opt_par_values,fval,exitflag,hessian_mat,options_,Scale]=dynare_minimize_objective(objective_function,start_par_value,minimizer_algorithm,options_,bounds,parameter_names,prior_information,Initial_Hessian,varargin)
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% Calls a minimizer
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%
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% INPUTS
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% objective_function [function handle] handle to the objective function
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% start_par_value [n_params by 1] vector of doubles starting values for the parameters
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% minimizer_algorithm [scalar double] code of the optimizer algorithm
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% options_ [matlab structure] Dynare options structure
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% bounds [n_params by 2] vector of doubles 2 row vectors containing lower and upper bound for parameters
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% parameter_names [n_params by 1] cell array strings containing the parameters names
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% prior_information [matlab structure] Dynare prior information structure (bayestopt_) provided for algorithm 6
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% Initial_Hessian [n_params by n_params] matrix initial hessian matrix provided for algorithm 6
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% varargin [cell array] Input arguments for objective function
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%
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% OUTPUTS
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% opt_par_values [n_params by 1] vector of doubles optimal parameter values minimizing the objective
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% fval [scalar double] value of the objective function at the minimum
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% exitflag [scalar double] return code of the respective optimizer
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% hessian_mat [n_params by n_params] matrix hessian matrix at the mode returned by optimizer
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% options_ [matlab structure] Dynare options structure (to return options set by algorithms 5)
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% Scale [scalar double] scaling parameter returned by algorith 6
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%
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% SPECIAL REQUIREMENTS
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% none.
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%
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%
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% Copyright (C) 2014-2015 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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%% set bounds and parameter names if not already set
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n_params=size(start_par_value,1);
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if isempty(bounds)
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if minimizer_algorithm==10
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error('Algorithm 10 (simpsa) requires upper and lower bounds')
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else
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bounds=[-Inf(n_params,1) Inf(n_params,1)];
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end
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end
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if minimizer_algorithm==10 && any(any(isinf(bounds)))
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error('Algorithm 10 (simpsa) requires finite upper and lower bounds')
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end
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if isempty(parameter_names)
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parameter_names=[repmat('parameter ',n_params,1),num2str((1:n_params)')];
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end
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%% initialize function outputs
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hessian_mat=[];
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Scale=[];
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exitflag=1;
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fval=NaN;
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opt_par_values=NaN(size(start_par_value));
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switch minimizer_algorithm
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case 1
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if isoctave
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error('Optimization algorithm 1 is not available under Octave')
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elseif ~user_has_matlab_license('optimization_toolbox')
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error('Optimization algorithm 1 requires the Optimization Toolbox')
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end
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% Set default optimization options for fmincon.
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optim_options = optimset('display','iter', 'LargeScale','off', 'MaxFunEvals',100000, 'TolFun',1e-8, 'TolX',1e-6);
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if ~isempty(options_.optim_opt)
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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if options_.analytic_derivation,
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optim_options = optimset(optim_options,'GradObj','on','TolX',1e-7);
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end
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[opt_par_values,fval,exitflag,output,lamdba,grad,hessian_mat] = ...
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fmincon(objective_function,start_par_value,[],[],[],[],bounds(:,1),bounds(:,2),[],optim_options,varargin{:});
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case 2
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error('Optimization algorithm 1 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
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case 3
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if isoctave && ~user_has_octave_forge_package('optim')
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error('Optimization algorithm 3 requires the optim package')
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elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
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error('Optimization algorithm 3 requires the Optimization Toolbox')
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end
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% Set default optimization options for fminunc.
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optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
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if ~isempty(options_.optim_opt)
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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if options_.analytic_derivation,
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optim_options = optimset(optim_options,'GradObj','on');
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end
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if ~isoctave
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[opt_par_values,fval,exitflag] = fminunc(objective_function,start_par_value,optim_options,varargin{:});
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else
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% Under Octave, use a wrapper, since fminunc() does not have a 4th arg
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func = @(x) objective_function(x,varargin{:});
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[opt_par_values,fval,exitflag] = fminunc(func,start_par_value,optim_options);
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end
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case 4
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% Set default options.
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H0 = 1e-4*eye(n_params);
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crit = options_.csminwel.tolerance.f;
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nit = options_.csminwel.maxiter;
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numgrad = options_.gradient_method;
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epsilon = options_.gradient_epsilon;
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% Change some options.
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'MaxIter'
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nit = options_list{i,2};
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case 'InitialInverseHessian'
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H0 = eval(options_list{i,2});
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case 'TolFun'
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crit = options_list{i,2};
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case 'NumgradAlgorithm'
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numgrad = options_list{i,2};
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case 'NumgradEpsilon'
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epsilon = options_list{i,2};
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otherwise
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warning(['csminwel: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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% Set flag for analytical gradient.
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if options_.analytic_derivation
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analytic_grad=1;
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else
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analytic_grad=[];
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end
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% Call csminwell.
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[fval,opt_par_values,grad,hessian_mat,itct,fcount,exitflag] = ...
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csminwel1(objective_function, start_par_value, H0, analytic_grad, crit, nit, numgrad, epsilon, varargin{:});
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case 5
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if options_.analytic_derivation==-1 %set outside as code for use of analytic derivation
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analytic_grad=1;
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crit = options_.newrat.tolerance.f_analytic;
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newratflag = 0; %analytical Hessian
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else
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analytic_grad=0;
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crit=options_.newrat.tolerance.f;
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newratflag = options_.newrat.hess; %default
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end
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nit=options_.newrat.maxiter;
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'MaxIter'
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nit = options_list{i,2};
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case 'Hessian'
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flag=options_list{i,2};
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if options_.analytic_derivation && flag~=0
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error('newrat: analytic_derivation is incompatible with numerical Hessian. Using analytic Hessian')
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else
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newratflag=flag;
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end
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case 'TolFun'
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crit = options_list{i,2};
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otherwise
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warning(['newrat: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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[opt_par_values,hessian_mat,gg,fval,invhess] = newrat(objective_function,start_par_value,analytic_grad,crit,nit,0,varargin{:});
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%hessian_mat is the plain outer product gradient Hessian
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if options_.analytic_derivation %Hessian is already analytic one, reset option
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options_.analytic_derivation = ana_deriv;
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end
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case 6
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[opt_par_values, hessian_mat, Scale, fval] = gmhmaxlik(objective_function, start_par_value, ...
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Initial_Hessian, options_.mh_jscale, bounds, prior_information.p2, options_.gmhmaxlik, options_.optim_opt, varargin{:});
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case 7
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% Matlab's simplex (Optimization toolbox needed).
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if isoctave && ~user_has_octave_forge_package('optim')
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error('Option mode_compute=7 requires the optim package')
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elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
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error('Option mode_compute=7 requires the Optimization Toolbox')
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end
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optim_options = optimset('display','iter','MaxFunEvals',1000000,'MaxIter',6000,'TolFun',1e-8,'TolX',1e-6);
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if ~isempty(options_.optim_opt)
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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[opt_par_values,fval,exitflag] = fminsearch(objective_function,start_par_value,optim_options,varargin{:});
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case 8
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% Dynare implementation of the simplex algorithm.
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simplexOptions = options_.simplex;
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'MaxIter'
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simplexOptions.maxiter = options_list{i,2};
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case 'TolFun'
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simplexOptions.tolerance.f = options_list{i,2};
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case 'TolX'
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simplexOptions.tolerance.x = options_list{i,2};
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case 'MaxFunEvals'
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simplexOptions.maxfcall = options_list{i,2};
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case 'MaxFunEvalFactor'
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simplexOptions.maxfcallfactor = options_list{i,2};
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case 'InitialSimplexSize'
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simplexOptions.delta_factor = options_list{i,2};
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otherwise
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warning(['simplex: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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[opt_par_values,fval,exitflag] = simplex_optimization_routine(objective_function,start_par_value,simplexOptions,parameter_names,varargin{:});
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case 9
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% Set defaults
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H0 = 1e-4*ones(n_params,1);
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cmaesOptions = options_.cmaes;
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% Modify defaults
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'MaxIter'
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cmaesOptions.MaxIter = options_list{i,2};
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case 'TolFun'
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cmaesOptions.TolFun = options_list{i,2};
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case 'TolX'
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cmaesOptions.TolX = options_list{i,2};
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case 'MaxFunEvals'
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cmaesOptions.MaxFunEvals = options_list{i,2};
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otherwise
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warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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warning('off','CMAES:NonfinitenessRange');
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warning('off','CMAES:InitialSigma');
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[x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),start_par_value,H0,cmaesOptions,varargin{:});
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opt_par_values=BESTEVER.x;
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fval=BESTEVER.f;
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case 10
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simpsaOptions = options_.simpsa;
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if ~isempty(options_.optim_opt)
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options_list = read_key_value_string(options_.optim_opt);
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for i=1:rows(options_list)
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switch options_list{i,1}
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case 'MaxIter'
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simpsaOptions.MAX_ITER_TOTAL = options_list{i,2};
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case 'TolFun'
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simpsaOptions.TOLFUN = options_list{i,2};
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case 'TolX'
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tolx = options_list{i,2};
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if tolx<0
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simpsaOptions = rmfield(simpsaOptions,'TOLX'); % Let simpsa choose the default.
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else
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simpsaOptions.TOLX = tolx;
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end
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case 'EndTemparature'
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simpsaOptions.TEMP_END = options_list{i,2};
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case 'MaxFunEvals'
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simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
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otherwise
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warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
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end
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end
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end
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simpsaOptionsList = options2cell(simpsaOptions);
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simpsaOptions = simpsaset(simpsaOptionsList{:});
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[opt_par_values, fval, exitflag] = simpsa(func2str(objective_function),start_par_value,bounds(:,1),bounds(:,2),simpsaOptions,varargin{:});
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case 11
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options_.cova_compute = 0 ;
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[opt_par_values,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(start_par_value,varargin{:}) ;
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case 101
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myoptions=soptions;
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myoptions(2)=1e-6; %accuracy of argument
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myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
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myoptions(5)= 1.0;
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[opt_par_values,fval]=solvopt(start_par_value,objective_function,[],myoptions,varargin{:});
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case 102
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%simulating annealing
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LB = start_par_value - 1;
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UB = start_par_value + 1;
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neps=10;
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% Set input parameters.
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maxy=0;
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epsilon=1.0e-9;
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rt_=.10;
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t=15.0;
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ns=10;
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nt=10;
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maxevl=100000000;
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idisp =1;
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npar=length(start_par_value);
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disp(['size of param',num2str(length(start_par_value))])
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c=.1*ones(npar,1);
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%* Set input values of the input/output parameters.*
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vm=1*ones(npar,1);
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disp(['number of parameters= ' num2str(npar) 'max= ' num2str(maxy) 't= ' num2str(t)]);
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disp(['rt_= ' num2str(rt_) 'eps= ' num2str(epsilon) 'ns= ' num2str(ns)]);
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disp(['nt= ' num2str(nt) 'neps= ' num2str(neps) 'maxevl= ' num2str(maxevl)]);
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disp ' ';
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disp ' ';
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disp(['starting values(x) ' num2str(start_par_value')]);
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disp(['initial step length(vm) ' num2str(vm')]);
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disp(['lower bound(lb)', 'initial conditions', 'upper bound(ub)' ]);
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disp([LB start_par_value UB]);
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disp(['c vector ' num2str(c')]);
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[opt_par_values, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparstart_par_valueam1,maxy,rt_,epsilon,ns,nt ...
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,neps,maxevl,LB,UB,c,idisp ,t,vm,varargin{:});
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otherwise
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if ischar(minimizer_algorithm)
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if exist(options_.mode_compute)
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[opt_par_values, fval, exitflag] = feval(options_.mode_compute,objective_function,start_par_value,varargin{:});
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else
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error('No minimizer with the provided name detected.')
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end
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else
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error(['Optimization algorithm ' int2str(minimizer_algorithm) ' is unknown!'])
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end
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end
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