ASKSAGE: Sage Q&A Forum - Individual question feedhttp://ask.sagemath.org/questions/Q&A Forum for SageenCopyright Sage, 2010. Some rights reserved under creative commons license.Wed, 02 Jul 2014 20:41:37 -0500convex objective function with linear constrainthttp://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/for a no convex objective function with linear constraint, a lagrangian relaxation should work?
Wed, 05 Oct 2011 14:46:01 -0500http://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/Comment by kcrisman for <p>for a no convex objective function with linear constraint, a lagrangian relaxation should work?</p>
http://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/?comment=21170#post-id-21170This 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?Thu, 06 Oct 2011 10:25:33 -0500http://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/?comment=21170#post-id-21170Answer by Gregory Bard for <p>for a no convex objective function with linear constraint, a lagrangian relaxation should work?</p>
http://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/?answer=23174#post-id-23174if 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.
Wed, 02 Jul 2014 20:41:37 -0500http://ask.sagemath.org/question/7489/convex-objective-function-with-linear-constraint/?answer=23174#post-id-23174