[Clp] Formulate a large scale linear programing model by reducing the number of similar constraints and keeping them all satisfied

[usa usa]

Hi, John,

I have run the code and do not understand how it can be used to reduce the
number of constraints in my problem.

Any guidance would be appreciated.

thanks

On Wed, Aug 31, 2016 at 1:29 PM, John Forrest <john.forrest at fastercoin.com>
wrote:

> Look at Clp/examples/dualCuts.cpp - you should be able to adapt it.
>
> John Forrest
>
>
> On 31/08/16 17:10, usa usa wrote:
>
> Hi,
>
>
>
> I need to build a large scale LP model and solve it by CLP.
>
>
>
> In the model, there is a kind of constraint like:
>
>
>
> Max: sum of (constantValueP_i  * decVarX_i) from i=1 to  N
>
> s.t.
>
>       decVarT + sum of (decVarK_i ) from i=1 to I = N  <=  [sum of
> (constantValueP_i  * decVarX_i)  from i=1 to  N ] * constantQ
>
>
>       [sum of (constantValueE_i  * decVarX_i)  from i=1 to  N ] <= [sum
> of (constantValueE_i )  from i=1 to  N ] * constantD
>
>
>       decVarK_1 >= sum of (constantValue_1_i  * decVarX_i) from i=1 to  N
> -
>
> decVarT
>
>
>       decVarK_2 >= sum of (constantValue_2_i  * decVarX_i) from i=1 to  N
> - decVarT
>
>
>       …
>
>
>       decVarK_L >= sum of (constantValue_j_i  * decVarX_i) from i=1 to  N
> - decVarT
>
>
>
> Decision variables:
>
> decVarT , 0 <= decVarX_i <= 1, decVarK_i >= 0
>
>
>
> The problem is that the number of constraints of   decVarK_i for i=1 to L
> and L can be very large, e.g. 100,0000.
>
>
>
> It means that it will have 100,000 constraints in the LP, which I want to
> avoid.
>
>
>
> How to combine them so that I can reduce the size of the LP model
> meanwhile keeping all constraints satisfied ?
>
>
>
> thanks
>
>
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