#20679: matrix vector product for generic sparse matrices
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Reporter: orkolorko | Owner:
Type: enhancement | Status: new
Priority: major | Milestone: sage-7.3
Component: linear algebra | Keywords: sparse, product
Merged in: | Authors:
Reviewers: | Report Upstream: N/A
Work issues: | Branch:
Commit: | Dependencies:
Stopgaps: |
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It seems like this simple cython code is faster 30% faster than the
implemented matrix vector product for generic sparse matrices.
I run some tests with matrices with RIF coefficients and all seem to point
in this direction.
%cython file
import cython
from sage.matrix.matrix_generic_sparse cimport *
from sage.modules.free_module_element cimport *
# this is a generic sparse_mat_vec, hoping to get some speedup from the
fact that it is compiled
def cython_sparse_matvec(Matrix_generic_sparse P,
FreeModuleElement_generic_dense v, FreeModuleElement_generic_dense w):
for (ij, val) in P.dict().iteritems():
w[ij[0]] += val*v[ij[1]]
def cython_sparse_vecmat(Matrix_generic_sparse P,
FreeModuleElement_generic_dense v, FreeModuleElement_generic_dense w):
for (ij, val) in P.dict().iteritems():
w[ij[1]] += val*v[ij[0]]
%python module
def sparse_matvec(P,v):
"""Compute Sage sparse matrix * vector product.
Apparently, M*v fills the matrix in Sage, so this should be quicker"""
w = VectorSpace(P.base_ring(),P.nrows())(0)
cython_sparse_matvec(P,v,w)
return w
def sparse_vecmat(v,P):
"""Compute Sage sparse vector * matrix product.
Apparently, v*M fills the matrix in Sage, so this should be quicker"""
w = VectorSpace(P.base_ring(),P.ncols())(0)
cython_sparse_vecmat(P,v,w)
return w
--
Ticket URL: <http://trac.sagemath.org/ticket/20679>
Sage <http://www.sagemath.org>
Sage: Creating a Viable Open Source Alternative to Magma, Maple, Mathematica,
and MATLAB
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