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? -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To post to this group, send email to sage-devel@googlegroups.com. To unsubscribe from this group, send email to sage-devel+unsubscr...@googlegroups.com. Visit this group at http://groups.google.com/group/sage-devel?hl=en.
