#13991: Mitigate speed regressions in symmetric function related code due to
#12313
---------------------------------+------------------------------------------
Reporter: nbruin | Owner: sage-combinat
Type: enhancement | Status: new
Priority: major | Milestone: sage-5.7
Component: combinatorics | Resolution:
Keywords: | Work issues:
Report Upstream: N/A | Reviewers:
Authors: | Merged in:
Dependencies: | Stopgaps:
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Comment (by nbruin):
For information, the results of `sage -tp devel/sage/sage/combinat/sf`:
{{{
sage -t devel/sage-0/sage/combinat/sf/all.py
[0.0 s] [0.0 s]
sage -t devel/sage-0/sage/combinat/sf/classical.py
[1.6 s] [1.3 s]
sage -t devel/sage-0/sage/combinat/sf/kschur.py
[1.9 s] [1.9 s]
sage -t devel/sage-0/sage/combinat/sf/dual.py
[1.9 s] [1.7 s]
sage -t devel/sage-0/sage/combinat/sf/elementary.py
[1.2 s] [1.4 s]
sage -t devel/sage-0/sage/combinat/sf/kfpoly.py
[1.3 s] [1.3 s]
sage -t devel/sage-0/sage/combinat/sf/schur.py
[1.3 s] [1.3 s]
sage -t devel/sage-0/sage/combinat/sf/sfa.py
[4.5 s] [12.2 s]
sage -t devel/sage-0/sage/combinat/sf/llt.py
[1.8 s] [2.2 s]
sage -t devel/sage-0/sage/combinat/sf/homogeneous.py
[1.2 s] [1.2 s]
sage -t devel/sage-0/sage/combinat/sf/multiplicative.py
[1.2 s] [1.1 s]
sage -t devel/sage-0/sage/combinat/sf/jack.py
[4.9 s] [6.3 s]
sage -t devel/sage-0/sage/combinat/sf/ns_macdonald.py
[1.8 s] [1.8 s]
sage -t devel/sage-0/sage/combinat/sf/monomial.py
[1.3 s] [1.3 s]
sage -t devel/sage-0/sage/combinat/sf/sf.py
[3.6 s] [7.8 s]
sage -t devel/sage-0/sage/combinat/sf/powersum.py
[1.2 s] [1.7 s]
sage -t devel/sage-0/sage/combinat/sf/orthotriang.py
[1.9 s] [1.9 s]
sage -t devel/sage-0/sage/combinat/sf/hall_littlewood.py
[2.9 s] [3.1 s]
sage -t devel/sage-0/sage/combinat/sf/__init__.py
[0.0 s] [0.0 s]
sage -t devel/sage-0/sage/combinat/sf/macdonald.py
[6.7 s] [13.9 s]
sage -t devel/sage-0/sage/combinat/sf/new_kschur.py
[5.5 s] [9.2 s]
sage -t devel/sage-0/sage/combinat/sf/k_dual.py
[9.6 s] [51.7 s]
}}}
First time is reference, second one with #12313. Variations have not been
compensated for, but the really big differences are probably due to a
genuine issue. As you can see, the slowdown in `k_dual.py` is dramatic.
Perhaps the other slowdowns give an indication for experts where the
biggest problem lies?
--
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/13991#comment:1>
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