Solve Linear Programming Homework: Global Min @ (0,0)

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SUMMARY

The discussion centers on solving a linear programming problem with a focus on determining the global minimum at the point (0,0). Participants clarify that the function in question is not linear and that the Hessian matrix must be analyzed to confirm positive semi-definiteness, which indicates convexity and the existence of a global minimum. The conclusion is that the original assumptions about the Hessian being positive semi-definite are incorrect, necessitating further differentiation to obtain the second derivatives for accurate analysis.

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  • Understanding of linear programming concepts
  • Knowledge of Hessian matrices and their properties
  • Familiarity with convex functions and global minima
  • Ability to differentiate functions to find second derivatives
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  • Learn about convex functions and their implications in optimization problems
  • Explore differentiation techniques for obtaining second derivatives
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Freydulf
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Homework Statement



Solve:

http://www.rinconmatematico.com/latexrender/pictures/0399b7f0b179dcbf396e72f315e6d219.png

Homework Equations



The Attempt at a Solution



http://www.rinconmatematico.com/latexrender/pictures/5a8e45f50b7d55e4c8ab2c5ce3b7d554.png
http://www.rinconmatematico.com/latexrender/pictures/dfd7077296a65ae4d3a0b0f409ef0118.png

http://www.rinconmatematico.com/latexrender/pictures/be995f308f56dfd08931544079d643eb.png

http://www.rinconmatematico.com/latexrender/pictures/76941dbf58eb6b6a6a5abb3f76c2326c.png
http://www.rinconmatematico.com/latexrender/pictures/8b328666878b3c586f55614fc7162373.png

http://www.rinconmatematico.com/latexrender/pictures/3846f3e2531e839b39ea7f5626b7c0ef.png

Positive-semidefinite, it has a global minimum at (0,0).


Well, that's what I've done til now. I'm not sure whether it's right, can someone give me a hand? :)
 
Last edited by a moderator:
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Can you explain what you're trying to do? It's hard to help without understanding your approach.


Freydulf said:
http://www.rinconmatematico.com/latexrender/pictures/be995f308f56dfd08931544079d643eb.png


Positive-semidefinite, it has a global minimum at (0,0).

I'm guessing you're trying to show that the function has a positive semi-definite Hessian, which implies convexity, which implies global minimum. However, what you have there is certainly not positive semidefinite.

http://www.rinconmatematico.com/latexrender/pictures/76941dbf58eb6b6a6a5abb3f76c2326c.png
http://www.rinconmatematico.com/latexrender/pictures/8b328666878b3c586f55614fc7162373.png

http://www.rinconmatematico.com/latexrender/pictures/3846f3e2531e839b39ea7f5626b7c0ef.png
Do you really believe this? You are essentially saying that both the function z^3 and the function -z^3 are both non-negative for all z.

Assuming you're trying to look at the Hessian, try differentiating again. It contains SECOND derivatives.
 
Last edited by a moderator:
This is certainly NOT "linear programming". Your equations are not linear.
 

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