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Let be defined for as:

1) Evaluate (upside down Delta) Jx.
2) Calculate HessJx .
3) Prove mathematically that J has a unique minimum.
4) a) We are given . Describe the algorithm of the gradiant of optimal step for this function J.
b) Prove mathematically that .
c) Deduce the scalar equation that needs to be solved at each iteration in order to obtain the step.


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Let be defined for X=(x,y) as

1) Evaluate ;

2) Calculate Hessian HessJ(X);
3) Prove mathematically J has a unique minimum;
Proof. Since HessJ(X)= , we know that
and . So HessJ(X) is a definite matrix . So, J has a unique minimum.
4) a)We are given a . Describe the algorithm of ...

Solution Summary

Optimization questions are answered. The expert proves mathematical that a function has a unique minimum. The solution is detailed and well presented.