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Re: [Help-glpk] Regarding speeding up MIPs by providing Initial solution


From: Ali Baharev
Subject: Re: [Help-glpk] Regarding speeding up MIPs by providing Initial solutions
Date: Tue, 19 Dec 2006 11:03:50 +0100

You could try the local branching heuristic (Fischetti-Lodi) to
optimize your initial solution, please see the attached pdf.

If you have a pretty fast heuristic to generate feasible solutions,
why don't you try genetic algorithm?

Ali

On 12/19/06, Andrew Makhorin <address@hidden> wrote:
>>From what I understand by looking at the GLPK manuals, it does provide you
> with the capability to specify a bound on the objective function value
> which avoids extra branching. For my problem, I have formulated it as a
> MIP optimization problem. Currently, it takes too long to solve it and I
> have tried almost all relevant options. The other approach that I think
> might work is to employ a heuristic to get a feasible solution and then
> feed the
> entire solution to GLPK. THe heuristic I have currently is very fast so if
> GLPK accepts an initial solution of the MIP and optimizes it furthur, one
> would expect the runtime to go down.
>
> Does GLPK let you accomplish this in some direct/indirect way?

Such feature is not implemented yet. However, it is easy to write
the initial incumbent objective value directly into the b&b data
structure. If you are interested in hacking, I can explain how to
do that.

Andrew Makhorin



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