Optimization based line fitting

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Hello!
I am using the following optimization script by which I am fitting the following curve between two points and
My question is, how can I solve the same optimization problem by adding the constraints ?
clc;
clear;
tic
%The Data: Time and response data
t = [0 10]';
y = [1 7]';
%Look at the Data
subplot(2,1,1)
plot(t,y,'*','MarkerSize',10)
grid on
xlabel('Time')
ylabel('Response')
hold on
%Curve to Fit
E = [ones(size(t)) exp(-t)]
E = 2×2
1.0000 1.0000 1.0000 0.0000
%Solving constrained linear least squares problem
% cNew = lsqlin(E,y,[],[],[1 1],y(1),[],[],[],opt) % Solver-based approach
p = optimproblem;
c = optimvar('c',2);
p.ObjectiveSense = 'minimize';
p.Objective = sum((E*c-y).^2);
% constraint example: p.Constraints.intercept = c(1) + c(2) == 0.82
sol = solve(p);
Solving problem using lsqlin.
cNew = sol.c;
tf = (0:0.1:10)';
Ef = [ones(size(tf)) exp(-tf)];
yhatc = Ef*cNew;
%plot the curve\
subplot(2,1,2)
plot(t,y,'*','MarkerSize',10)
grid on
xlabel('Time')
ylabel('Response')
hold on
plot(tf,yhatc)
title('y(t)=c_1 + c_2e^{-t}')
toc
Elapsed time is 2.581019 seconds.
  1 个评论
Matt J
Matt J 2021-4-27
编辑:Matt J 2021-4-27
how can I solve the same optimization problem by adding the constraints ...?
The constraint is already satisfied by the solution that you have. What will it accomplish to formalize the constraint in the optimization?

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采纳的回答

Matt J
Matt J 2021-4-27
编辑:Matt J 2021-4-27
p = optimproblem;
c = optimvar('c',2);
p.ObjectiveSense = 'minimize';
p.Objective = sum((E*c-y).^2);
p.Constraints=[1,exp(-5)]*c>=4;
sol = solve(p);
  1 个评论
Simon Reclapino
Simon Reclapino 2021-6-25
Thanks for your support. @Matt J
Could you also please help me in setting a constraint on the derivative. e.g. . However, if you'd like to, you can take a look on the detailed question in the link

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