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nonlinear minimization with fminunc

조회 수: 3 (최근 30일)
Daniel
Daniel 2014년 7월 24일
댓글: Shashank Prasanna 2014년 7월 24일
Hi All:
I am doing parameterization by minimizing a nonlinear target function. However, after the iteration runs, it returns the following message. And returns with the initial values for the parameters that I set.
*Iteration Func-count f(x) Step-size optimality
0 9 1.46536 7.77e+06
1 144 1.39431 1.57417e-14 5.47e+06
Local minimum possible.
fminunc stopped because the size of the current step is less than the default value of the step size tolerance.*
There is no error in the code. What do you suggest to solve this issue?
Thank you!

답변 (2개)

Matt J
Matt J 2014년 7월 24일
Evaluate the gradient at the initial point and see if it is close to zero. Also, call fminunc with all of its output arguments,
[x,fval,exitflag,output,grad,hessian]= fminunc(...)
to get more diagnostic information.
  댓글 수: 2
Daniel
Daniel 2014년 7월 24일
Thanks for your comments.
Gradient: 1.1230 -0.5735 -0.7339 -2.8473 -0.4665 -0.7594 -1.5631 -2.0650
Hessian: -0.3929 -0.0146 1.6484 1.8609 1.4881 2.0857 -0.6735 2.4585 -0.0146 1.9118 4.0316 3.6329 2.6916 2.6806 -0.9732 6.1937 1.6484 4.0316 0.3282 0.9016 1.4807 4.5981 -1.3104 3.2933 1.8609 3.6329 0.9016 3.0804 3.2111 2.4965 1.9743 0.3590 1.4881 2.6916 1.4807 3.2111 0.5078 1.3144 0.0943 1.4038 2.0857 2.6806 4.5981 2.4965 1.3144 2.8224 0.0054 1.7383 -0.6735 -0.9732 -1.3104 1.9743 0.0943 0.0054 1.2775 3.2253 2.4585 6.1937 3.2933 0.3590 1.4038 1.7383 3.2253 2.3437
How do you think?
Thanks!
Matt J
Matt J 2014년 7월 24일
편집: Matt J 2014년 7월 24일
I'm guessing your function may not be differentiable. A local minimum of a twice continuously differentiable function should have a positive semi-definite Hessian and gradient near zero. It appears you are far from either.

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Shashank Prasanna
Shashank Prasanna 2014년 7월 24일
편집: Shashank Prasanna 2014년 7월 24일
The optimization stopped because size of the current step is less than the default value. However you can change the defaults.
I suggest you read the following articles in the link below:
  • When the Solver Fails
  • When the Solver Might Have Succeeded
  • When the Solver Succeeds
There are guidelines on what you can try in each of the situations.
  댓글 수: 2
Daniel
Daniel 2014년 7월 24일
Thanks Shashank. I actually noticed that and fixed it with changing the default step size. But now, got another message:
fminunc stopped because it cannot decrease the objective function along the current search direction.
... looked online support, feels like there is really no solution for this...
Do you have any idea about this?
Thanks!
Shashank Prasanna
Shashank Prasanna 2014년 7월 24일
fminunc is a derivative based optimizer. If you have discontinuous objective surface or have multiple optimums then fminunc becomes sensitive to initial start points. If you do have an exotic objective function I recommend trying multistart or patternsearch which does better at finding "global" optimum solutions.
Local vs Global:

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