convex objective function with linear constraint
for a no convex objective function with linear constraint, a lagrangian relaxation should work?
1 Answer
if f(x1,x2,..,xn) is convex (or concave) then minimizing or maximizing subject to the linear constraint g(x1,x2,..,xn)=0 should be the same as minimizing or maximizing
F(x1,x2,...,xn,L) = f(x1,x2,...xn) + L*g(x1,x2,...,xn)
in the unconstrained sense. See "minimize?" for help. Convex functions (or concave) functions will have a unique minimum (or unique maximum), so everything should work out.
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Asked: 2011-10-05 21:46:01 +0200
Seen: 288 times
Last updated: Jul 03 '14
This question is fairly vague, which is presumably why someone downvoted it. Can you give a more explicit formulation of your question? For instance, are you asking how to find "Lagrangian relaxations" in Sage?