#18607: Speed-up for __contains__ in linear codes
-------------------------------------+-------------------------------------
Reporter: dlucas | Owner:
Type: enhancement | Status: positive_review
Priority: major | Milestone: sage-6.8
Component: coding theory | Resolution:
Keywords: | Merged in:
Authors: David Lucas | Reviewers:
Report Upstream: N/A | Work issues:
Branch: | Commit:
u/dlucas/speedup_in_contains | 6319903d843188bfa2dfdc1083a3b9fc25ec6a8c
Dependencies: | Stopgaps:
-------------------------------------+-------------------------------------
Changes (by jsrn):
* status: needs_review => positive_review
Old description:
> The actual implementation of `__contains__` for linear codes is quite
> slow.
> It can be improved using the syndrome computation instead of checking
> if the vector belongs to a specific subspace of the ambient space.
>
> Test:
> {{{
> F = GF(1009)
> n, k = 1000, 500
> C = codes.RandomLinearCode(n, k, F)
>
> subspace = []
> syndrome = []
>
> for i in range(20):
> c = C.random_element()
> start = time.clock()
> A = C.ambient_space()
> S = A.subspace(C.gens())
> res = S.__contains__(c)
> elapsed = (time.clock() - start)
> assert res == True
> subspace.append(elapsed)
>
> start = time.clock()
> if not v in C.ambient_space() or len(v) != C.length():
> res = False
> else:
> res = (C.syndrome(c) == 0)
> elapsed = (time.clock() - start)
> assert res == True
> syndrome.append(elapsed)
> }}}
>
> Results:
> {{{
> sage: median(subspace)
> 1.526604500000019
> sage: median(syndrome)
> 0.00408399999997755
> }}}
New description:
The actual implementation of `__contains__` for linear codes is quite
slow.
It can be improved using the syndrome computation instead of checking
if the vector belongs to a specific subspace of the ambient space.
Test:
{{{
F = GF(1009)
n, k = 1000, 500
C = codes.RandomLinearCode(n, k, F)
subspace = []
syndrome = []
for i in range(20):
c = C.random_element()
start = time.clock()
A = C.ambient_space()
S = A.subspace(C.gens())
res = S.__contains__(c)
elapsed = (time.clock() - start)
assert res == True
subspace.append(elapsed)
start = time.clock()
if not c in C.ambient_space() or len(c) != C.length():
res = False
else:
res = (C.syndrome(c) == 0)
elapsed = (time.clock() - start)
assert res == True
syndrome.append(elapsed)
}}}
Results:
{{{
sage: median(subspace)
1.526604500000019
sage: median(syndrome)
0.00408399999997755
}}}
--
Comment:
So if the author has not changed his mind as a consequence of my comments,
I give this the green light. All tests pass and documentation builds.
(I fixed the bug in the test code)
--
Ticket URL: <http://trac.sagemath.org/ticket/18607#comment:6>
Sage <http://www.sagemath.org>
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