I have a very abstract model where a set of coefficients controls animal behavior. This model is so abstract that the actual values of a global extrema are not particularly interesting. However, the number of local extrema is extremely interesting (If anyone cares why just ask and I'll explain). I figured the biggest drawback of gradient ascent/ descent, that it can get stuck at local extrema, is actually an advantage for me.

My plan was to first run the gradient ascent algorithm with all the coefficients set to 0 (i.e. G(0) ) Then I do G(1). All the coefficients have a range of [0, 1]. If both runs result in the same point, then I have conclude that only one local maxima exists. If they result in different points, then I run G(mean of those points - small value) and (mean of those points + small value). Repeat until all local maxima have been found.

I know that this plan will give me all the local maxima in 1 dimension (pretty sure we proved this in my Calc II course). What I am unsure about is in more dimensions than that.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.