Undefined function for input arguments of type 'double': how do I make them vectors and not doubles?
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For an assignment I'm working on, I was asked to write an input function that would calculate the Hessian of a particular function using the Jacobian approximation - i.e., . Then, have the code I was given for Newton's method call this input function, and the result would essentially be the same as running Gauss-Newton's method on it instead.
The problem is, every time I attempt to run it, I get error messages telling me "Undefined function 'jacobian' for input arguments of type 'double'." How do I modify my code so that it doesn't think that the function "val" which is supposed to be a function of three variables:
is of type double, and that it knows it is supposed to take both the gradient and the Jacobians with respect to the vector (but it needs to be coded as x(1), x(2), x(3) in order for my Newton's Method function to accept it.
Here is the code of my input function (thank you for your time and patience):
function[val,g,H]=givenfGNM(x)
%givenf() modified to output the Hessian approximated by the Jacobian, as
%required by the Gauss-Newton Method
val=(1/2)*((2*x(1)-(x(2)*x(3))-1)^2+(1-x(1)+x(2)-exp(x(1)-x(3)))^2+(-x(1)-2*x(2)+3*x(3))^2);
if(nargout>1)
g=gradient(val, x);
end
if(nargout>2)
J=jacobian(val, x);
K=transpose(J);
H=mtimes(K,J);
end
end
9 件のコメント
Adam Danz
2019 年 9 月 9 日
Your hesitation is healthy! :) Hesitation in the face of doubt is much better than the very common mistake we often see when people who have no idea what they are doing start making messy changes to working code.
Nested functions usually go at the end of the main function. Here is some introductory info on nested functions :
Save a backup of your working code so 1) you can revert to it if needed and 2) so you can compare the outputs from your updated code to the older version as a means of a sanity check.
"I suppose I could also set the values in the function m file each time... ...But that would be incredibly contrived"
Ineeded it would be contrived so don't do that. If you need additional inputs, add them to the nested funtion. For example, in Matt J's answer, you could even add the objective function handle as an input if you think the objective function itself might change.
回答 (2 件)
Matt J
2019 年 9 月 9 日
編集済み: Matt J
2019 年 9 月 9 日
Here is the non-symbolic approach mentioned by Adam.
function[val,g,H]=givenfGNM(x)
%givenf() modified to output the Hessian approximated by the Jacobian, as
%required by the Gauss-Newton Method
givenf=@(z) (1/2)*((2*z(1)-(z(2)*z(3))-1)^2+(1-z(1)+z(2)-ezp(z(1)-z(3)))^2+(-z(1)-2*z(2)+3*z(3))^2);
val=givenf(x);
opts=optimoptions('lsqnonlin','MaxIter',1,'Display','none',...
'Algorithm','levenberg-marquardt');
[~,~,~,~,~,~,J]=lsqnonlin(givenf,x,[],[],opts);
g=J.';
H=J.'*J;
end
2 件のコメント
Adam Danz
2019 年 9 月 14 日
You'll see that the 7th output is the jacobian. You don't need outputs 1:6 so to get the 7th output without assigning outputs 1:6 to variables, you can use tildes (~) as place holders.
Similarly, "opts" is the 5th input but you don't necessarily need to specify the lower and upper bounds (3rd and 4th inputs) (or maybe you do need to specify them - that's up to you). With many functions an empty input results in using the default values.
To summarize, use tildes for output place holders and square brackets for input place holders in most functions.
David Hill
2019 年 9 月 8 日
function[val,g,H]=givenfGNM(x)
syms a b c
val=(1/2)*((2*a-(b*c)-1)^2+(1-a+b-exp(a-c))^2+(-a-2*b+3*c)^2);
g=gradient(val, [a b c]);
if(nargout==3)
J=jacobian(val, [a b c]);
K=transpose(J);
H=mtimes(K,J);
a=x(1);
b=x(2);
c=x(3);
H=double(subs(H));
end
a=x(1);
b=x(2);
c=x(3);
g=double(subs(g));
val=double(subs(val));
end
If you do have symbolic toolbox, you should be able to produce the gradient and jacobian of your function and evaluate them whenever you want using the subs function.
9 件のコメント
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