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convex objective function with linear constraint

asked 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?

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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?

kcrisman gravatar imagekcrisman ( 2011-10-06 10:25:33 -0500 )edit

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answered 2014-07-02 20:41:37 -0500

Gregory Bard gravatar image

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 14:46:01 -0500

Seen: 142 times

Last updated: Jul 02 '14