Improved iterations display of Dynare's simplex routine.
Stéphane Adjemian(Charybdis)
parent
2d321cb0e3
commit
44c49eaef8
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@ -49,6 +49,13 @@ function [x,fval,exitflag] = simplex_optimization_routine(objective_function,x,o
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% Set verbose mode
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verbose = options.verbosity;
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% Set header
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if verbose
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header = sprintf('#Iter. #FnCalls. Best Value Worst Value Norm(f) Norm(x) Move');
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iter_ = ' ';
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fval_ = ' ';
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end
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% Set number of control variables.
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number_of_variables = length(x);
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@ -179,27 +186,23 @@ trend_vector_2 = 2:(number_of_variables+1);
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% Set initial simplex around the initial guess (x).
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if verbose
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skipline(3)
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disp('+----------------------+')
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disp(' SIMPLEX INITIALIZATION ')
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disp('+----------------------+')
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skipline()
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disp('Simplex initialization...')
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end
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initial_point = x;
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[initial_score,junk1,nopenalty] = feval(objective_function,x,varargin{:});
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if ~nopenalty
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disp('Cannot initialize the simplex with the provided initial guess.')
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skipline()
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error('simplex_optimization_routine:: Initial condition is wrong!')
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else
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[v,fv,delta] = simplex_initialization(objective_function,initial_point,initial_score,delta,zero_delta,1,varargin{:});
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disp('Done!')
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skipline()
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func_count = number_of_variables + 1;
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iter_count = 1;
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if verbose
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disp(['Objective function value: ' num2str(fv(1))])
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disp(['Current parameter values: '])
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fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
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for i=1:number_of_variables
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fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',var_names{i},v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
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end
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skipline()
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end
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end
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@ -220,10 +223,15 @@ tooslow = 0;
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iter_no_improvement_break = 0;
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max_no_improvement_break = 1;
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if verbose
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disp(header)
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end
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critF = 1.0;
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critX = 1.0;
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while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex_algo_iterations<=max_simplex_algo_iterations)
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% Do we really need to continue?
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critF = max(abs(fv(1)-fv(trend_vector_2)));
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critX = max(max(abs(v(:,trend_vector_2)-v(:,unit_vector))));
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if critF <= max(f_tolerance,10*eps(fv(1))) && critX <= max(x_tolerance,10*eps(max(v(:,1))))
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convergence = 1;
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end
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@ -258,10 +266,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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func_count = func_count+1;
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if fxe < fxr% xe is even better than xr.
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if optimize_expansion_parameter
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if verbose>1
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skipline(2)
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disp('Compute optimal expansion...')
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end
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% Compute optimal expansion...
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xee = xbar + rho*chi*1.01*(xbar-v(:,end));
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x = xee;
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fxee = feval(objective_function,x,varargin{:});
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@ -271,14 +276,8 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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weight = rho*chi*1.02;
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fxeee_old = fxee;
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xeee_old = xee;
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if verbose>1
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fprintf(1,'Weight = ');
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end
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while decrease
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weight = 1.02*weight;
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if verbose>1
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fprintf(1,'\b\b\b\b\b\b\b %6.4f',weight);
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end
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xeee = xbar + weight*(xbar-v(:,end));
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x = xeee;
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fxeee = feval(objective_function,x,varargin{:});
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@ -290,9 +289,6 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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decrease = 0;
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end
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end
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if verbose>1
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fprintf(1,'\n');
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end
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xe = xeee_old;
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fxe = fxeee_old;
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else
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@ -300,14 +296,8 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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weight = rho*chi;
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fxeee_old = fxee;
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xeee_old = xee;
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if verbose>1
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fprintf(1,'Weight = ');
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end
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while decrease
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weight = weight/1.02;
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if verbose>1
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fprintf(1,'\b\b\b\b\b\b\b %6.4f',weight);
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end
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xeee = xbar + weight*(xbar-v(:,end));
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x = xeee;
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fxeee = feval(objective_function,x,varargin{:});
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@ -319,20 +309,15 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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decrease = 0;
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end
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end
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if verbose>1
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fprintf(1,'\n');
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end
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xe = xeee_old;
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fxe = fxeee_old;
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end
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if verbose>1
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disp('Done!')
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skipline(2)
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end
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move = 'expand-opt';
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else
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move = 'expand';
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end
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v(:,end) = xe;
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fv(end) = fxe;
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move = 'expand';
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else% if xe is not better than xr.
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v(:,end) = xr;
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fv(end) = fxr;
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@ -383,37 +368,37 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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% Sort n+1 points by incresing order of the objective function values.
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[fv,sort_idx] = sort(fv);
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v = v(:,sort_idx);
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critF = max(abs(fv(1)-fv(trend_vector_2)));
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critX = max(max(abs(v(:,trend_vector_2)-v(:,unit_vector))));
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if verbose
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if ~mod(simplex_iterations, 50)
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skipline()
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disp(header)
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end
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iter = int2str(simplex_iterations);
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fval = int2str(func_count);
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iter_(1:length(iter)) = iter;
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fval_(1:length(fval)) = fval;
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if isfinite(fv(end)) && isfinite(fv(1))
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if fv(end)<0
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disp(sprintf('%s %s %12.7E %12.7E %12.7E %12.7E %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
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else
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if fv(1)>0
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disp(sprintf('%s %s %12.7E %12.7E %12.7E %12.7E %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
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else
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disp(sprintf('%s %s %12.7E %12.7E %12.7E %12.7E %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
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end
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end
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else
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if isfinite(fv(1))
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disp(sprintf(['%s %s %12.7E %12.7E %s'], iter_, fval_, fv(1) , critX, move))
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else
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disp(sprintf('%s %s %12.7E %s', iter_, fval_, critX, move))
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end
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end
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end
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iter_count = iter_count + 1;
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simplex_iterations = simplex_iterations+1;
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if verbose>1
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disp(['Simplex iteration number: ' int2str(simplex_iterations) '-' int2str(simplex_init) '-' int2str(simplex_algo_iterations)])
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disp(['Simplex move: ' move])
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disp(['Objective function value: ' num2str(fv(1))])
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disp(['Mode improvement: ' num2str(best_point_score-fv(1))])
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disp(['Norm of dx: ' num2str(norm(best_point-v(:,1)))])
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disp(['Norm of dSimplex: ' num2str(norm(v-vold,'fro'))])
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disp(['Crit. f: ' num2str(critF)])
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disp(['Crit. x: ' num2str(critX)])
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skipline()
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end
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if verbose && max(abs(best_point-v(:,1)))>x_tolerance
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if verbose<2
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disp(['Simplex iteration number: ' int2str(simplex_iterations) '-' int2str(simplex_init) '-' int2str(simplex_algo_iterations)])
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disp(['Objective function value: ' num2str(fv(1))])
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disp(['Mode improvement: ' num2str(best_point_score-fv(1))])
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disp(['Norm of dx: ' num2str(norm(best_point-v(:,1)))])
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disp(['Norm of dSimplex: ' num2str(norm(v-vold,'fro'))])
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disp(['Crit. f: ' num2str(critF)])
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disp(['Crit. x: ' num2str(critX)])
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skipline()
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end
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disp(['Current parameter values: '])
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fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
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for i=1:number_of_variables
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fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',var_names{i}, v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
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end
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skipline()
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end
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if abs(best_point_score-fv(1))<f_tolerance
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no_improvements = no_improvements+1;
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else
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@ -424,7 +409,9 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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vold = v;
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if no_improvements>max_no_improvements
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if verbose
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skipline()
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disp(['NO SIGNIFICANT IMPROVEMENT AFTER ' int2str(no_improvements) ' ITERATIONS!'])
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skipline()
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end
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if simplex_algo_iterations<=max_simplex_algo_iterations
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% Compute the size of the simplex
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@ -432,12 +419,9 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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% Compute the new initial simplex.
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[v,fv,delta] = simplex_initialization(objective_function,best_point,best_point_score,delta,zero_delta,1,varargin{:});
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if verbose
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disp(['(Re)Start with a lager simplex around the based on the best current '])
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disp(['values for the control variables. '])
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disp(['New value of delta (size of the new simplex) is: '])
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for i=1:number_of_variables
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fprintf(1,'%s: \t\t\t %+8.6f \n',var_names{i}, delta(i));
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end
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disp('(Re)Start with a lager simplex based on the best current values for the control variables.')
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skipline()
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disp(header)
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end
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% Reset counters
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no_improvements = 0;
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@ -446,22 +430,27 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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iter_no_improvement_break = iter_no_improvement_break + 1;
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simplex_init = simplex_init+1;
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simplex_iterations = simplex_iterations+1;
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skipline(2)
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end
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end
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if ((func_count==max_func_calls) || (iter_count==max_iterations) || (iter_no_improvement_break==max_no_improvement_break) || convergence || tooslow)
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[v,fv,delta] = simplex_initialization(objective_function,best_point,best_point_score,DELTA,zero_delta,1,varargin{:});
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if func_count==max_func_calls
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if verbose
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skipline()
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disp(['MAXIMUM NUMBER OF OBJECTIVE FUNCTION CALLS EXCEEDED (' int2str(max_func_calls) ')!'])
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skipline()
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end
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elseif iter_count== max_iterations
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if verbose
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skipline()
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disp(['MAXIMUM NUMBER OF ITERATIONS EXCEEDED (' int2str(max_iterations) ')!'])
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skipline()
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end
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elseif iter_no_improvement_break==max_no_improvement_break
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if verbose
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skipline()
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disp(['MAXIMUM NUMBER OF SIMPLEX REINITIALIZATION EXCEEDED (' int2str(max_no_improvement_break) ')!'])
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skipline()
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end
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iter_no_improvement_break = 0;
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if simplex_algo_iterations==max_simplex_algo_iterations
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@ -469,9 +458,13 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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continue
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end
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elseif tooslow
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skipline()
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disp(['CONVERGENCE NOT ACHIEVED AFTER ' int2str(simplex_iterations) ' ITERATIONS! IMPROVING TOO SLOWLY!'])
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skipline()
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else
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skipline()
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disp(['CONVERGENCE ACHIEVED AFTER ' int2str(simplex_iterations) ' ITERATIONS!'])
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skipline()
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end
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if simplex_algo_iterations<max_simplex_algo_iterations
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% Compute the size of the simplex
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@ -479,12 +472,9 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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% Compute the new initial simplex.
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[v,fv,delta] = simplex_initialization(objective_function,best_point,best_point_score,delta,zero_delta,1,varargin{:});
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if verbose
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disp(['(Re)Start with a lager simplex around the based on the best current '])
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disp(['values for the control variables. '])
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disp(['New value of delta (size of the new simplex) is: '])
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for i=1:number_of_variables
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fprintf(1,'%s: \t\t\t %+8.6f \n',var_names{i}, delta(i));
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end
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disp('(Re)Start with a lager simplex based on the best current values for the control variables.')
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skipline()
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disp(header)
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end
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% Reset counters
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func_count=0;
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@ -495,7 +485,6 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
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iter_count = iter_count+1;
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simplex_iterations = simplex_iterations+1;
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simplex_algo_iterations = simplex_algo_iterations+1;
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skipline(2)
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else
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break
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end
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