Any idea of how to do what you've proposed (same order of magnitude for different parameters) ? I am using a non-analytic 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.
"John D'Errico" <firstname.lastname@example.org> wrote in message <email@example.com>... > "Hanan Shteingart" <firstname.lastname@example.org> wrote in message <email@example.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 re-normalize 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