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karl
Posts:
228
Registered:
8/11/06


Re: Reconstruct determinant of the Hessian
Posted:
Dec 17, 2012 9:51 AM


Am 17.12.2012 12:02, schrieb Peter Spellucci: > karl <oudeis@nononet.com> writes: >> Hi all, >> >> in quasi Newton methods in optimization the Hessian of the target function is reconstructed. >> This means that one needs to store a matrix in each step. >> If one needs only the *determinant* of the Hessian, is >> there a trick to get it without storing the whole matrix? >> >> Thanks >> >> Karl > no! > clearly there is a recursive formula for the determinant, quite easy to > apply if you use the rankonemultiplicative form of the update. > but how would you compute the next "s" and the next "y" needed for this > without computing them? and in order to compute them you need the matrix. > and the ''limited memory'' version also needs to store old vectors, and due > to its numerical instability works not well for longer memorys > sorry, no help > peter > > Thanks, I guessed that there is no way. Karl



