[AMPL 2592] Running time - ILP

[ankit-3]

Hello,

I am using CPLEX to solve ILP, which consists of 40 constraints.
Solving ILP in cplex, even for a small input to the problem, is taking
very long time. Is there any other approach to solve the ILP by which
i can reduce the running time?

Thanks
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[tamingsari83]

hi there,

Why not u try to use GNU Linear programming because it use the same data file as AMPL or you can use another program called LPSolve IDE. Both free to download. Hopefully this will helps u.

-suli-



----- Original Message ----
From: ankit <electronics.ankit@...>
To: AMPL Modeling Language <ampl@...>
Sent: Saturday, June 13, 2009 10:36:27 PM
Subject: [AMPL 2592] Running time - ILP


Hello,

I am using CPLEX to solve ILP, which consists of 40 constraints.
Solving ILP in cplex, even for a small input to the problem, is taking
very long time. Is there any other approach to solve the ILP by which
i can reduce the running time?

Thanks


     

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[Paul A. Rubin]

On Jun 13, 5:36 pm, ankit <electronics.an...@...> wrote:
>
> I am using CPLEX to solve ILP, which consists of 40 constraints.

With an ILP, it's typically the number of integer variables, rather
than the number of constraints, that drives execution time.  (That's
an oversimplification; the structure of the constraints also has a lot
to do with it.)

> Solving ILP in cplex, even for a small input to the problem, is taking
> very long time. Is there any other approach to solve the ILP by which
> i can reduce the running time?
>

The short answer is "maybe".  The long answer involves years of study
of ILP models, copious practice, and periodic sacrifices to the Gods
of Optimization.

For some problems, there are multiple valid formulations, and one
formulation may solve much faster than another.  Changing the
formulation is possible within AMPL (assuming you can think of a
better formulation). Adding seemingly redundant constraints sometimes
helps (if they are redundant in the ILP but tighten the LP
relaxation).

Changing solver parameters can also help with some models.  CPLEX's
default settings work pretty well on most problems, but on any
specific problem you may be able to improve performance by tweaking
the parameters.  The documentation for the AMPL/CPLEX combination
should list CPLEX-specific parameters and explain how to change them
in AMPL.

For some problems, there are better algorithmic approaches (Benders
decomposition, Lagrangean relaxation, ...) than simple off-the-shelf
branch-and-cut.  This is something you cannot do easily in AMPL,
although it is possible to script some "master-slave" approaches in
AMPL.  For particularly difficult problems, you may have to write your
own code using one of the APIs (C, C++, Java, C#) to the CPLEX
library.

Unfortunately, there is no one-size-fits-all answer for speeding up
ILPs.  If there were, it would already be built into AMPL or CPLEX.

/Paul
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