#7740 closed defect (fixed)
Speed up MixedIntegerLinearProgram
Reported by: | Nathann Cohen | Owned by: | jkantor |
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Priority: | major | Milestone: | sage-4.3.1 |
Component: | numerical | Keywords: | |
Cc: | Robert Miller | Merged in: | sage-4.3.1.alpha2 |
Authors: | Nathann Cohen | Reviewers: | Robert Miller |
Report Upstream: | N/A | Work issues: | |
Branch: | Commit: | ||
Dependencies: | Stopgaps: |
Description
From Robert Miller :
sage: from sage.graphs.graph_coloring import vertex_coloring sage: g = graphs.CirculantGraph(120, [2,3,5,7]) sage: vertex_coloring(g)
It takes longer to set up the constraint than to solve the problem, on my laptop.
Attachments (1)
Change History (13)
comment:1 Changed 13 years ago by
Summary: | Spped up MixedIntegerLinearProgram → Speed up MixedIntegerLinearProgram |
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comment:2 Changed 13 years ago by
Status: | new → needs_info |
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comment:3 Changed 13 years ago by
Status: | needs_info → needs_review |
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This patch adds to the file numerical.mip a class LinearFunction? which avoid using the much more general symbolic expressions from Sage ( as we only need to define linear functions ).
Before :
sage: from sage.graphs.graph_coloring import vertex_coloring sage: g = graphs.CirculantGraph(120, [2,3,5,7]) sage: timeit("vertex_coloring(g)") 5 loops, best of 3: 3.94 s per loop
After :
sage: from sage.graphs.graph_coloring import vertex_coloring sage: g = graphs.CirculantGraph(120, [2,3,5,7]) sage: timeit("vertex_coloring(g)") 5 loops, best of 3: 2.18 s per loop
The next way to speed up this class would be, methinks, to cythonize it. I tried it this time but got stuck by the fact that the solving functions ( solveCoin, solveGlpk ) are not directly included in Sage and installed by the packages... The best way would really be to move these sources into Sage. It would also solve solve the problem of having to update both packages and numerical.mip t the same time .. :-/
Nathann
comment:4 Changed 13 years ago by
Status: | needs_review → needs_work |
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comment:5 Changed 13 years ago by
Status: | needs_work → needs_review |
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Before :
sage: g = graphs.WorldMap() sage: %timeit g.edge_connectivity() 10 loops, best of 3: 1.29 s per loop
After :
sage: g = graphs.WorldMap() sage: %timeit g.edge_connectivity() 10 loops, best of 3: 231 ms per loop
But as mentionned earlier, we will have to find other ways to improve this class ! :-)
Nathann
comment:6 Changed 13 years ago by
Status: | needs_review → needs_work |
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Looks good to me! Aside from this typo: "An elementary algebra algebra"
Changed 13 years ago by
Attachment: | trac_7740.patch added |
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comment:8 Changed 13 years ago by
Authors: | → Nathann Cohen |
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Merged in: | → 4.3.1.alpha2 |
Resolution: | → fixed |
Reviewers: | → Robert Miller |
Status: | needs_review → closed |
positive review
comment:10 Changed 13 years ago by
Merged in: | 4.3.1.alpha2 → sage-4.3.1.alpha2 |
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comment:11 Changed 13 years ago by
Hi Nathan,
Sorry to pop up late into the discussion. What was the rationale for not using CombinatorialFreeModule?(whatever_ring, ZZ)?
For the record, I very much hope that FreeModule?(ring, infinity, sparse = True) will be available sometime soon. That will be a faster alternative.
comment:12 Changed 13 years ago by
Hello !
At first I used InfinitePolynomialRing?, then plain "vars", then I just wondered why it was still very slow and just wondered what it would give if I were to write the symbolics myself to understand... As it was easy enough, I wrote something to try it on my computer, and ended up writing a patch to send the code.
This way, it stores the informations in a format that is optimal for what I need ( no powers --only linear functions--, sparse from the beginning, ... ). Since, I have also noticed that having my own symbolics would let me define expressions like double inequalities :
0 < a + b < 9
Which I had been missing for a long time.. :-) The main problem I have is that in many cases, the symbolics take most of the time spent on the computation of a matching (or other LP problems), which is quite disturbing ;-)
Nathann
Well, this time is spent as expected on the add_constraint function, which may spend time over considerations coming from symbolic computations, though I did not achieve to know where... When I am profiling your example I see :
These functions are the ones responsible for the time spent defining the LP, but I could not find which line of add_constraint is calling them... If you have any idea, please tell me and I'll give it a look :-)