Gaurav,
It is possible if C1 and C2 are nonnegative:
create a continuous 'max' variable, then for each possible (i,j) pair
add a specific constraint:
C1*Xij + C2*Yij <= max
now, by solving min(max), you'll actually minimize the maximum C1*Xij +
C2*Yij for all (i,j).
François
Gaurav Khanna a écrit :
Hi,
I am trying to solve a 0-1 linear optimization problem
with linear constraints. I am explaining the problem i
am facing with a small example. lets say there are
three variables i am solving for Xij, Yij and Zij.
Each of the X,Y, and Z are indexed by i and j where i
varies from 1 to 10, j varies from 1 to 100. The
function f is defined as
f = (for all i max( C1*Xij + C2*Yij)) and the goal
function is min(f) subject to a set of constraints on
Xij and Yij. Is this possible to do in GLPK.? The
reason i am asking this is because GLPK requires
entering the coeffecients for each of the terms in the
goal function .However, here i cant determine the
coefficients since my goal function which is to be
minimized is itself a maximum over multiple functions.
thanx
gaurav
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