|
From: | usa usa |
Subject: | [Help-glpk] Formulate a large scale linear programing model by reducing the number of similar constraints and keeping them all satisfied |
Date: | Wed, 31 Aug 2016 12:10:40 -0400 |
Hi,
I need to build a large scale LP model and solve it by GLPK.
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
[Prev in Thread] | Current Thread | [Next in Thread] |