lsqnonlin (lsqcurvefit , fmincon) does not change the variables in an optimization process to find the best fit
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Esi Tesi
am 25 Mär. 2024
Kommentiert: Torsten
am 2 Apr. 2024
I'm perfoming an optimization of parameters. The main difference between my work and a regular curvefiiting problem is that the function that should be fitted is one that obtains x-y data from a third party simulation software. Here is the main section of the code:
u_test = []; f_test = []; % Anything
x0 = [22,280];
force = @(x) sim_res(x,u_test,f_test) - f_test;
x = lsqnonlin(force,x0,[4,199],[40,350]);
and here is the function that goes into another software (ABAQUS) and obtains some simulation results (a set of x-y data):
function force = sim_res(x,u_test,f_test)
inp_initial(x(1),x(2)); % Replaces new coefficients for new iteration
pause(2)
system('abaqus job=Al.inp user=NONLINEAR.for cpus=4 interactive' )
pause(2)
while exist('Al.lck','file')==2 % Some files have to be deleted
pause(0.1)
end
while exist('Al.odb','file')==0 % Some files have to be deleted
pause(0.1)
end
[dis,force] = Read_ODB_outputs_node(); % Another function that obtains results
plot(dis,force,u_test,f_test)
% Some files have to be deleted
delete('Al.prt');
delete('Al.com');
delete('Al.sim');
delete('Al.dat');
delete('Al.log');
end
The problem is that lsqnonlin does not change the initial values of x and after 2-3 iterations, the process stops and converges. The same happens with lsqcurvefit and fmincon. Can anyone find a solution for this?
Thanks in advance
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Akzeptierte Antwort
Torsten
am 25 Mär. 2024
Verschoben: Torsten
am 25 Mär. 2024
We don't know if you get modified values for the array "force" from "ABAQUS" if the input vector "x" to "sim_res" is slightly changed by "lsqnonlin". If this is not the case, "lsqnonlin" will think that changing the parameters does not change the objective function and stops - usually with the message that the initial point is optimal.
13 Kommentare
Torsten
am 2 Apr. 2024
I don't know the reason. Most probably, the values coming from ABAQUS are too inexact. This would cause that computing reliable derivatives with respect to the parameters being fitted fails.
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