# convex objective function with linear constraint

for a no convex objective function with linear constraint, a lagrangian relaxation should work?

convex objective function with linear constraint

asked
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2011-10-05 14:46:01 -0500
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for a no convex objective function with linear constraint, a lagrangian relaxation should work?

0

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.

Asked: **
2011-10-05 14:46:01 -0500
**

Seen: **143 times**

Last updated: **Jul 02 '14**

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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?