Max min, Lagrange's multiplier question

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SUMMARY

The discussion centers on solving a Lagrange multiplier problem involving the function S = f(x1, x2, ..., x2006) = x1 * 1^(1/3) + x2 * 2^(1/3) + ... + x2006 * 2006^(1/3) with the constraint G(x1, x2, ..., x2006) = x1^(3/2) + x2^(3/2) + ... + x2006^(3/2) - (2^(1/2) / (2006^(1/2) * 2007^(1/2))) = 0. The solution process involves setting the gradients equal, specifically ∇S = λ ∙ ∇G, leading to a system of 2007 linear equations. The next steps require calculating the gradients of S and G to derive the necessary equations for solving the problem.

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Homework Statement


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Homework Equations


possibly Lagrange's multiplier..

The Attempt at a Solution


treating S = f(x1, x2, ... , x2006) = x1 * 1^1/3 + x2 * 2^1/3 + ... + x2006 * 2006^1/3

and constrain G(x1, x2 ... x2006) = x1 ^ 3/2 + x2 ^ 3/2 + ... + x2006 ^ 3/2 - (2^1/2 / (2006^1/2 * 2007 ^ 1/2)) = 0

then solve G(x...) = 0
gradient(f) = lambda * gradient(G) , which isn't all that clear what to do next.. any hints ?
 
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It isn't clear what to do next? How about completing that equation:
[itex]\nabla S= \lambda \cdot \nabla G[/itex]? (S, not f)
What is [itex]\nabla S[/itex]? What is [itex]\nabla G[/itex]? That should give you 2007 linear equations for x1, x2, . . . , x2006 and [itex]\lambda[/itex]. Fortunately they are almost all separated and solving just a few should give you the general formula.
 

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