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
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