checkGradients, but the objective function has two inputs: x and xdata?

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Benjamin
Benjamin 2024-11-14
评论： Torsten 2024-11-21
I'm using lsqcurvefit with the following objective function and Jacobian:
function [f, jacF] = semiCircle(p, Q)
P0 = p(1);
Q0 = p(2);
r = p(3);
f = P0 + sqrt(r^2 - (Q-Q0).^2);
if nargout > 1 % need Jacobian
jacF = [1, (Q-Q0)./sqrt(r^2-(Q0-Q).^2), r./sqrt(r^2-(Q0-Q).^2)];
end
end
I'd like to use checkGradients to verify if the Jacobian is correct. However, all of the examples in the documentation just have objective functions with one input, the parameters 'x'. Whereas my function semiCircle has two inputs: the parameters 'p' and the xdata 'Q'. Is there a way to use checkGradients for such a function?

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Torsten
Torsten 2024-11-14
valid = checkGradients(@(p)semiCircle(p, Q),p0)
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Benjamin
Benjamin 2024-11-21
编辑：Torsten 2024-11-21
Ran in:
I reached my daily uploads limit, so I'll just put the functions here:
load('data.mat')
Vb_ll_rms = 690;
% Inital guess
p10 = 5e6;
p20 = 2.5e7;
p30 = 3e7;
p0 = [p10, p20, p30];
[Rls, Xls, Vgls, gradientCheck] = lsqcurvefitNLS(p0, Q, P, Vb_ll_rms)
Rls = 0.0024
Xls = 0.0238
Vgls = 689.9365
gradientCheck = logical
1
function [R, X, Vg, gradientCheck] = lsqcurvefitNLS(p0, Q, P, Vo)
% Box constraints
p1_ub = min(P);
p2_lb = max(Q);
p3_lb = max(Q) - min(Q);
lb = [0, p2_lb, p3_lb];
ub = [p1_ub, inf, inf];
% Linear constraints
A = [0, 1, -1];
b = min(Q);
gradientCheck = checkGradients(@(p)semiCircle(p,Q),p0);
options = optimoptions('lsqcurvefit','Display','off','SpecifyObjectiveGradient',true);
p = lsqcurvefit(@semiCircle, p0, Q, P, lb, ub, A, b, [], [], [], options);
P0 = p(1);
Q0 = p(2);
r = p(3);
R = P0/(P0^2 + Q0^2)*Vo^2;
X = Q0/(P0^2 + Q0^2)*Vo^2;
Vg = sqrt(r^2/(P0^2 + Q0^2)*Vo^2);
end
And the other one:
function [f, jacF] = semiCircle(p, Q)
P0 = p(1);
Q0 = p(2);
r = p(3);
f = P0 + sqrt(r^2 - (Q-Q0).^2);
if nargout > 1 % need Jacobian
jacF = zeros(length(Q), length(p));
for i = 1:length(Q)
jacF(i,:) = [1, (Q(i)-Q0)/sqrt(r^2-(Q0-Q(i))^2), r/sqrt(r^2-(Q0-Q(i))^2)];
end
end
end
Torsten
Torsten 2024-11-21
As you said: the code works fine with R2024b.
But note that the call to "lsqcurvefit" has changed in R2023a to the actual call that you use in the code. So if your desktop MATLAB version is older than R2023a, linear constraints (A,b) are not yet accepted.

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