Suppose I have a problem
min
f(x)
Subject To
h(x) <= 0
To make h(x) a quadratic soft constraint, we could add:
min
f(x) + λ*s^2
Subject To
h(x) - s <= 0
s >= 0
or
min
f(x) + λ*t
Subject To
h(x) - s <= 0
s^2 <= t
s, t >= 0
Is there any reason a priori to believe one formulation will be superior in terms of CPLEX performance or stability?
Thanks.
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Michael Han
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#DecisionOptimization