# Optimization

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

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

1) Evaluate ;

2) Calculate Hessian HessJ(X);

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 ...

###### Education

- BSc , Wuhan Univ. China
- MA, Shandong Univ.

###### Recent Feedback

- "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
- "excellent work"
- "Thank you so much for all of your help!!! I will be posting another assignment. Please let me know (once posted), if the credits I'm offering is enough or you ! Thanks again!"
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