[Clp] Dense LP Problem Interface

[Miles Lubin]

Hi Matt,

You may want to try out the linprog function in Julia:

http://mathprogbasejl.readthedocs.org/en/latest/linprog.html

All you do is:

using MathProgBase
using Clp
sol = linprog(c, Acon, '<', rhsvec, -Inf, Inf, ClpSolver())

You can also easily switch in commercial solvers. Unfortunately I don't
know of any well supported solvers which are optimized for such large dense
problems.

Miles

On Wed, Jul 8, 2015 at 9:53 AM, Matt Bromberg <mattcbro at earthlink.net>
wrote:

> I have yet to see a simple way to use CBC or CLP using raw binary data.
> In particular I have a large problem with 40K constraints 10K variables or
> so.
> That's about 3Gbytes of raw doubles.  It expands to an unwieldy size using
> any of the text base input formats. (hundreds of gigabytes).
>
> I can get that data into a python numpy array but it does not appear to be
> easy to dump that raw array into CLP.  Unfortunately cylp doesn't support
> the latest API.
> I would roll my own python interface but there is literally no
> documentation I can find online for the C interface to something like
> CoinMP.
>
> All the other interfaces appear to be useless for my purposes.  Pulp is
> rather arcane and there is no obvious way to pull the data back into python.
>
> I am in somewhat disbelief that I can't do this:
> [xopt,fmin] = linprog(c, Acon, rhsvec) ;
>
> to solve min c' * x  given Acon * x <= rhsvec .
>
>  The above is the one line matlab interface to linprog.  There should be
> something similar in Python in support of CLP using it's primary matrix
> array interface, numpy/ndarrays.
> Even my cylp solution takes 10 lines of python code to do this, plus half
> a day of parsing documentation.  It's just unfortunate that it segfaults on
> the larger problems.
>
> If anyone has a good way to do this in Python or C, with an example I'd
> love to see it.  For now I have to move on to other solvers.
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