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lsqcurvefit versus fminsearchbnd
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Matthew
Nov 3, 2011, 3:39:11 PM11/3/11
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When is it appropriate to use lsqcurvefit versus fminsearchbnd?
I am fitting around 11 parameters, and lsqcurvefit seems to be superior. I am simply inquiring why.
Thanks
I am fitting around 11 parameters, and lsqcurvefit seems to be superior. I am simply inquiring why.
Thanks
Alan Weiss
Nov 4, 2011, 10:29:42 AM11/4/11
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much faster and more reliable. fminsearchbnd is based on fmransearch,
which uses the Nelder-Mead simplex algorithm. This algorithm does not
use gradients, is poor in more than a few dimensions, and is not
guaranteed to converge.
Alan Weiss
MATLAB mathematical toolbox documentation
John D'Errico
Nov 4, 2011, 12:46:13 PM11/4/11
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"Matthew" <matthew.mer...@utsouthwestern.edu> wrote in message <j8uqkv$6rg$1...@newscl01ah.mathworks.com>...
bounded cousin, fminsearchbnd, on large problems. "Large" is of
course in the eye of the beholder, but a typical upper limit of
practicality is somewhere around 6 variables, with 3 variables a
better realistic limit for efficient behavior.
11 variables is well beyond my limit in any case.
John
> When is it appropriate to use lsqcurvefit versus fminsearchbnd?
>
> I am fitting around 11 parameters, and lsqcurvefit seems to be superior. I am simply inquiring why.
>
> Thanks
In addition to Alan's comments, I will NEVER use fminsearch, or its
>
> I am fitting around 11 parameters, and lsqcurvefit seems to be superior. I am simply inquiring why.
>
> Thanks
bounded cousin, fminsearchbnd, on large problems. "Large" is of
course in the eye of the beholder, but a typical upper limit of
practicality is somewhere around 6 variables, with 3 variables a
better realistic limit for efficient behavior.
11 variables is well beyond my limit in any case.
John
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