# 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
**
2011-10-05 14:46:01 -0600
**

This post is a wiki. Anyone with karma >750 is welcome to improve it.

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 -0600
**

Seen: **139 times**

Last updated: **Jul 02 '14**

Copyright Sage, 2010. Some rights reserved under creative commons license. Content on this site is licensed under a Creative Commons Attribution Share Alike 3.0 license.

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?