This is the mail archive of the gsl-discuss@sources.redhat.com mailing list for the GSL project.


Index Nav: [Date Index] [Subject Index] [Author Index] [Thread Index]
Message Nav: [Date Prev] [Date Next] [Thread Prev] [Thread Next]
Other format: [Raw text]

Re: Help: working with large gsl_vector


Toan T Nguyen wrote:
I'm using the multidimensional minimization procedure in GSL and have problem with large vectors. The dimensionality of my vectors is 300000 which means my index variable is of long integer type.

Hum. I authored the initial version of the multidimensional minimization functions which have been improved by others, but I don't think they have been improved in such a way that optimization in dimension 300 000 is possible. Even for a quadratic function and using a conjugate gradient algorithm, it will take 3e5 iterations to the algorithm to complete. As each iteration implies a linear search for minimum that will use at least around 10 evaluations of your function, your a looking for 3e6 function evaluation. Unless you have a very special problem, I would guess that a function evaluation take at least 3e5 operations to complete, which implies 9e11 operations (a.k.a. 2^39 operations). That's a lot. And we are talking about a quadratic form, remember ?


Perhaps you could tell us a little bit more about your application ?

Fabrice

PS : Support Vector Machine algorithms have proven that it is indeed possible to do quadratic optimization under constraints in high dimensional space (like 10000 dimensions), but they use _very_ complex methods.


Index Nav: [Date Index] [Subject Index] [Author Index] [Thread Index]
Message Nav: [Date Prev] [Date Next] [Thread Prev] [Thread Next]