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karl
Posts:
213
Registered:
8/11/06
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Re: Reconstruct determinant of the Hessian
Posted:
Dec 17, 2012 9:51 AM
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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 rank-one-multiplicative 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
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