[SciPy-user] Need Help: Linear Programming
nicky van foreest
Tue Dec 23 08:15:32 CST 2008
Please keep me informed. I had pulp working within a few minutes, even
though I am not a good python programmer. Hopefully it works for you
2008/12/23 Aditya Sethi <email@example.com>:
> Hi Nick
> I will try pulp.py. Sounds like it does what I need to do. I am currently
> using cvxopt and its working.
> In addition I will be looking at Horst's cython files.
> Thanks for your reply!! I will check out the differences bw pulp and cvxopt.
> On Mon, Dec 22, 2008 at 5:48 PM, nicky van foreest <firstname.lastname@example.org>
>> I use pulp.py as an interface to produce a file that can be read by
>> XPRESS (a commercial package) to solve my LP problems. Pulp can also
>> produce output for GLPK (which if free), CPLex, and, perhaps, symphony
>> (which is also free), but I did not try the latter. Pulp is, at least
>> for my purposes, really useful. it allows me to specify the cost
>> function and the constraint in an efficient and readable way.
>> 2008/12/22 Robert Kern <email@example.com>:
>> > On Sun, Dec 21, 2008 at 23:43, Aditya Sethi <firstname.lastname@example.org> wrote:
>> >> Thanks Robert for the prompt reply.
>> >> I would use the LP Solver Hoyt mentioned and try the options in
>> >> OpenOpt.
>> >> But it surprises me that there are a number of non-linear optimization
>> >> solvers, but no LP Solvers. How come?
>> > LP solvers are much harder to write. Well, "good" implementations of
>> > an LP solver are more complicated than "good" implementations of a
>> > standard nonlinear local optimizer. The comparison isn't really
>> > apples-to-apples, though. A good LP solver can have much higher
>> > dimensionality than a good nonlinear optimizer will deal with. But
>> > because of this, a typical LP *problem* will also have many more
>> > dimensions, too.
>> > --
>> > Robert Kern
>> > "I have come to believe that the whole world is an enigma, a harmless
>> > enigma that is made terrible by our own mad attempt to interpret it as
>> > though it had an underlying truth."
>> > -- Umberto Eco
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