simplex_nonlinear.m

function [opt_data,model] = simplex_nonlinear(model,varargin)
%function opt_data = simplex_nonlinear(model,varargin)
%
% optimization function optimizing the model using the matlab optimization
% technique "fminsearch" (which uses a nonlinear simplex algorithm).
% 
% varargin: can be model_data or redeuced_data
%
%
% Generated fields of opt_data:
% optimal_params:   solution to the parameter optimization problem
% nr_fct_calls:     number of functional evaluations
% output:           optimal output

% Markus Dihlmann 11.12.12


opt_data = [];

init_params = model.optimization.init_params;
tolerance = model.optimization.tol;
if strcmp(model.optimization.min_or_max,'max')
    min_or_max = -1;
else
    min_or_max = 1;
end

os = optimset('TolFun',tolerance);

if isfield(varargin{1},'grid')
    %--> detailed simulations
    model_data = varargin{1};
    
    [mu_opt,f_end,exitflag,output_data] =...
        fminsearch(@(mu)(min_or_max*model.optimization.objective_function(model, model_data,mu)),...
        init_params,os);
        
    
else
    %--> reduced simulations
    reduced_data = varargin{1};
    
    [mu_opt,f_end,exitflag,output_data] =...
        fminsearch(@(mu)(min_or_max*model.optimization.objective_function(model, reduced_data,mu)),...
        init_params,os);
    
    %error estimator
    model = set_mu_to_optimize(model,mu_opt);
    model = setfield_in_model_and_base_model(model,'compute_derivative_info',1);
    
    [grad_f_x, Delta_grad] = model.optimization.get_Jacobian(model,reduced_data);
    
    
    %bedingung überprüfen
    g_H = model.gamma_H(model);
    L_H = model.L_H(model);
    break_value = 2*(g_H)^2*(L_H)*(norm(grad_f_x)+norm(Delta_grad(:,end)));
    if break_value<1
        Delta_mu = 2*g_H*norm(norm(grad_f_x)+norm(Delta_grad(:,end)));
    else
        disp('No error estimator available. Missing neccessary requirements.')
        Delta_mu=-1;
    end
        
    opt_data.Delta_mu = Delta_mu;
    opt_data.break_value = break_value;
end


opt_data.optimal_params = mu_opt;
opt_data.nr_fct_calls = output_data.funcCount;
opt_data.nr_iterations = output_data.iterations;
opt_data.exit_flag = exitflag;
opt_data.output = f_end;