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Re: parameter normalization for optimization functions such as fmincon and fminsearch
Posted:
Jun 25, 2009 1:23 PM


Thanks John for your quick reply.
Any idea of how to do what you've proposed (same order of magnitude for different parameters) ? I am using a nonanalytic function that measure the likelihood of a model to an observed measurements, so how can I tell in advance the derivation of the likelihood by each parameters? I don't have an explicit expression for the likelihood since the model is not stationary and is data dependent.
Thanks, Hanan.
"John D'Errico" <woodchips@rochester.rr.com> wrote in message <h20959$i6u$1@fred.mathworks.com>... > "Hanan Shteingart" <chanansh@gmail.com> wrote in message <h208cv$s5u$1@fred.mathworks.com>... > > Hi, > > From other discussions I've seen here, it seems the TolX parameter in the option parameter to fmincon/fminsearch is absolute, which means those functions assume the same order of magnitude to all parameters. Is there any smart way to solve this? Should I normalized all parameters to be around [0 1] and renormalize it within the subject function (which I want to minimize)? > > > > Better than normalizing all parameters to be unity, would > be to scale them to have derivatives that are all roughly > the same order of magnitude. > > Lacking that, scaling them to be roughly the same > magnitude may be reasonable. > > John



