Hi Jan,

it is my feeling that Nils is absolutely right with his remark.
I looked myself quickly through 
http://www.sagemath.org/doc/reference/sage/matrix/matrix_integer_dense.html 
and what is written there about LLL --- there are at least two number 
theoretic packages mentioned there, that Sage relies on ("... NTL library 
by Victor Shoup or fpLLL library by Damien Stehle ...") and several choices 
of which floating point number representations to use, each of which has 
its purpose ('fp' - double precision: NTL’s FP or fpLLL’s double, 'qd' - 
quad doubles: NTL’s QP, 'xd' - extended exponent: NTL’s XD or fpLLL’s dpe, 
'rr' - arbitrary precision: NTL’RR or fpLLL’s MPFR").
So my first guess had been, that it is not only important to allow for 
several different internal algorithms and different internal 
representations, but to let the user choose them.

Then I read the post from Nils, and I think that he's absolutely right 
about the fact that what you really want from the structure of RR^n is 
"only" the inner product --- so indeed you need not to store the location 
of the basis vectors "inside RR^n", but your basis vectors are just the 
standard ones ([1, 0, 0, ...], [0, 1, 0, ...], ...) in ZZ^n, and all you 
need to carry around is the Gram matrix (i.e. the information about the 
inner product), in order to be able to calculate and compare the "length" 
of vectors, the volume of a Voronoi cell, and such.

After all, if you finally want to go for something like "lattices over 
general Dedekind domains" and apply algorithms to find "shortest length 
vectors", you'll need a notion of "length" there ... and the general way to 
encode such information are "inner products".

So I guess I leave this discussion and let the others continue, that know 
more about the topic.


Cheers,
Georg

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