Re: [eigen] cache-friendly matrix inverse

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It's actually something I never considered before: for me, as a
mathematician, matrix inversion is a basic operation that "of course"
i consider important, without even asking myself why; if you ask about
practical uses for it, well I'm pretty sure that there must be
situations where you want to solve Ax=b for a single given matrix A,
but for a lot of vectors b and you don't know in advance what these
vectors b will be, you just want to be able to solve very fast
whenever they show up; then the best thing you can do is to precompute
A^-1 so you only have a matrix-vector product to compute whenever a
"b" vector shows up.


2009/5/13, Christian Mayer <mail@xxxxxxxxxxxxxxxxx>:
> Hash: SHA256
> Robert Lupton the Good schrieb:
>> I'm not sure that this is still on-topic, but if you have a model
>> [...] least-squares estimate [...]
>> So you really need to invert a matrix.  You can invert it any
>> way you like (e.g. eigen-value decomposition; Gaussian elimination; ...)
>> but invert it you must.
> Well, I knew that you don't need to calculate an explicit inverse there
> (it would have surprised me that such a standard task would invalidate
> the no-inversion-in-numerics rule).
> You need a SVD in this case. Anyway, as soon as you've got a numerical
> task to solve, it's allways a good idea to have a look at the numerical
> recepies book, in this case Chapter 15.4:
> This leaves the question unanswered if these few posts are off-topic?
> I guess: no. It's just a discussion about a usecase of Eigen ;)
> CU,
> Chris
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