Too many output parameters error in Fsolve when I turn Jacobian On

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farzad 2019-6-6

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评论： Walter Roberson 2019-6-7

Hi all

why do I get Too many output parameters error in Fsolve when I turn Jacobian On ? I have 9 variables to solve , where can be the problem ? and does the Jacobian matrix calculation help me if this is the iteration report ? :

 Iteration  Func-count     f(x)          step         optimality    radius
       10    6.09647e+006                     3.27e+005               1
       20    5.00637e+006              1      2.98e+005               1
       30     2.7289e+006            2.5      2.24e+005             2.5
       40         37481.6           6.25      1.99e+004            6.25
       50         5.77916        1.28439            192            15.6
       60    2.82227e-007      0.0169644         0.0368            15.6
       70    1.34282e-021   4.00165e-006      3.17e-009            15.6
       80    8.78879e-025   2.98649e-013      1.58e-010            15.6
     
     

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Geoff Hayes 2019-6-6

farzad - please show the code that generates this error so that we can see what you are passing into the function and what you are expecting as outputs. The error messages is telling you that are expecting more output parameters than will actually be returned by the function.

farzad 2019-6-7

Sorry can't share the code, I am solving for 9 variables, are they too many for Matlab 2007 ?

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Answer 1

Walter Roberson 2019-6-7

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When you turn on Jacobian, then you need to return the Jacobian as a second output, but only when it is asked for. You need to test nargout for that.

function [obj, J] = fun_to_solve(x)
   obj = x.^2 - 10*x + 5;
   if nargout > 1
       J = 2.*x - 10;
   end
end

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farzad 2019-6-7

Thank you

Here you habe one function and the jacobian is the derivative of that one function, in my case I have muktuple functions, how Wil my Jacobian look like?

Walter Roberson 2019-6-7

https://www.mathworks.com/help/optim/ug/fsolve.html#buta__s-fun

"If fun returns a vector (matrix) of m components and x has length n, where n is the length of x0, the Jacobian J is an m-by-n matrix where J(i,j) is the partial derivative of F(i) with respect to x(j). (The Jacobian J is the transpose of the gradient of F.)"

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