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Large memory consumption by the basis of the ambient space
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More options Aug 30 2012, 3:23 am
From: Thomas Feulner <thomas.feul...@uni-bayreuth.de>
Date: Thu, 30 Aug 2012 00:23:28 -0700 (PDT)
Local: Thurs, Aug 30 2012 3:23 am
Subject: Large memory consumption by the basis of the ambient space

Hi,

I am working on linear codes and I observed a high memory consumption when
constructing codes of large length. I figured out that this problem already
appears in the construction of vector spaces:

sage: F.<a> = GF(4)
sage: M = MatrixSpace(F, 8, 10000).random_element()
sage: V = VectorSpace(F, M.ncols())
sage: V.__dict__.keys()
['__reduce_ex__', '_element_class', '_gram_matrix',
'_FreeModule_generic__rank', '_FreeModule_generic__is_sparse',
'_FreeModule_generic__degree',
'_FreeModule_generic__uses_ambient_inner_product']
sage: V.subspace(M)
Vector space of degree 10000 and dimension 8 over Finite Field in a of size
2^2
Basis matrix:
8 x 10000 dense matrix over Finite Field in a of size 2^2
sage: V.__dict__.keys()
['__reduce_ex__', '_element_class', '_gram_matrix',
'_FreeModule_generic__rank', '_FreeModule_generic__is_sparse',
'_FreeModule_generic__degree', '_FreeModule_ambient__basis',
'_FreeModule_generic__uses_ambient_inner_product']

As you can see, the construction of the subspace adds a basis to V ==> sage
stores 10000 dense vectors of length 10000!

Doing the same construction over other fields, say F=QQ, does not show this
behavior. Is there any
solution in sight for this?

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More options Aug 30 2012, 4:38 am
From: Thomas Feulner <thomas.feul...@uni-bayreuth.de>
Date: Thu, 30 Aug 2012 01:38:47 -0700 (PDT)
Local: Thurs, Aug 30 2012 4:38 am
Subject: Re: Large memory consumption by the basis of the ambient space

An update:

The ticket
http://trac.sagemath.org/sage_trac/ticket/13304
does not solve this problem.