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

[John Forrest]

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 toN
>
> s.t.
>
> decVarT + sum of (decVarK_i ) from i=1 to I = N<=[sum of 
> (constantValueP_i* decVarX_i)from i=1 toN ] * constantQ
>
>
> [sum of (constantValueE_i* decVarX_i)from i=1 toN ] <= [sum of 
> (constantValueE_i )from i=1 toN ] * constantD
>
>
> decVarK_1 >= sum of (constantValue_1_i* decVarX_i) from i=1 toN -
>
> decVarT
>
>
> decVarK_2 >= sum of (constantValue_2_i* decVarX_i) from i=1 toN - decVarT
>
>
> …
>
>
> decVarK_L >= sum of (constantValue_j_i* decVarX_i) from i=1 toN - decVarT
>
> Decision variables:
>
> decVarT , 0 <= decVarX_i <= 1, decVarK_i >= 0
>
> The problem is that the number of constraints ofdecVarK_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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