Argument passing issue.
3 views (last 30 days)
Show older comments
Ba Ba Black Sheep!
on 3 Jan 2017
Edited: Walter Roberson
on 3 Jan 2017
I have a requirement that, I use the following modified Rosenbrock function,
where the coefficients a and b are to be read from a text file.
.
I tried the following,
function out = rosenbrock(x)
disp('rosenbrock()..........called');
coeff = load('coeff.txt');
a = coeff(1);
b = coeff(2);
xx = x(1);
yy = x(2);
out = (1 - xx + a)^2 + 100*(yy - b - (xx-a)^2)^2;
end
But it is doing two things,
(1) Slowing down the performance of the optimization.
(2) The output is also not correct (the optimization isn't converging).
How can I solve this issue wile fulfilling my requirement?
Is it possible to pass the values of a and b as the arguments of rosenbrockwithgrad()?
Relevant Source Code
function [x, fval, eflag, iter, fcount] =
Optimization_With_Analytic_Gradient(start_point)
x0 = start_point;
% inline function defitions
%fungrad = @(x)deal(fun(x),grad(x));
% options setup
options = optimoptions( 'fminunc', ...
'Display','off',...
'OutputFcn',@bananaout,...
'Algorithm','trust-region', ...
'GradObj','on');
% calling fminunc
[x, fval, eflag, output] = fminunc(@rosenbrockwithgrad, x0, options);
iter = output.iterations;
fcount = output.funcCount;
% plot window title
title 'Rosenbrock with Analytic Gradient...'
disp('Optimization_With_Analytic_Gradient...');
end
function out = gradient( x )
out = [-400*(x(2) - x(1)^2)*x(1) - 2*(1 - x(1));
200*(x(2) - x(1)^2)];
end
function [f,g] = rosenbrockwithgrad(x)
% Calculate objective f
f = rosenbrock(x);
if nargout > 1 % gradient required
g = gradient(x);
end
end
0 Comments
Accepted Answer
Walter Roberson
on 3 Jan 2017
2 Comments
Walter Roberson
on 3 Jan 2017
Edited: Walter Roberson
on 3 Jan 2017
function [x, fval, eflag, iter, fcount] =
Optimization_With_Analytic_Gradient(start_point, a, b)
x0 = start_point;
% options setup
options = optimoptions( 'fminunc', ...
'Display','off',...
'OutputFcn',@bananaout,...
'Algorithm','trust-region', ...
'GradObj','on');
% calling fminunc
[x, fval, eflag, output] = fminunc(@(x) rosenbrockwithgrad(x, a, b), x0, options);
iter = output.iterations;
fcount = output.funcCount;
% plot window title
title 'Rosenbrock with Analytic Gradient...'
disp('Optimization_With_Analytic_Gradient...');
end
function out = gradient( x, a, b )
%this routine probably needs to be changed to take a and b into account
out = [-400*(x(2) - x(1)^2)*x(1) - 2*(1 - x(1));
200*(x(2) - x(1)^2)];
end
function [f,g] = rosenbrockwithgrad(x, a, b)
% Calculate objective f
f = rosenbrock(x, a, b);
if nargout > 1 % gradient required
g = gradient(x, a, b);
end
end
More Answers (0)
See Also
Categories
Find more on Surrogate Optimization in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!