On 11/12/10 10:30 PM, Robert Bradshaw wrote:
On Fri, Nov 12, 2010 at 7:10 PM, Jason Grout
It only has to construct an element if it can't figure out what to do
after consulting the Parents themselves.
Ah, okay.
And then there's the matter you talk about; why is an element so big?
The example above is quite strange. No idea why it should be so big.
(Note that these are are arbitrary precision integers, so the relative
overhead for small ones is still quite large).
As for the original example,
sage: type(vector(RR, range(100)))
<type 'sage.modules.free_module_element.FreeModuleElement_generic_dense'>
Which means that each element is a full Python RealNumber, quite a bit
more than a double. Still doesn't explain the memory usage. For almost
any kind of linear algebra, you're better off using RDF, or even numpy
directly.
RDF also seems to have a big problem:
sage: v=vector(RDF, range(10000))
sage: get_memory_usage()
206.84765625
sage: w=v.parent().an_element()
sage: get_memory_usage()
983.60546875
This is very strange, as RDF vectors are literally light wrappers around
numpy arrays. I wonder how much Sage library code has to be loaded to
do this conversion?
Note that an_element() is cached in the parent. Still, that seems odd
that it goes up by ~780MB.
Jason
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