Hessian matrix in taylor expansion help

In summary, the function has critical points at x=0, y=0, and z=0 and solving the system of equations for these values yields a minimum at (0,0,0).
  • #1
sdevoe
21
0

Homework Statement



Find the critical point(s) of this function and determine if the function has a maxi-
mum/minimum/neither at the critical point(s) (semi colons start a new row in the matrix)

f(x,y,z) = 1/2 [ x y z ] [3 1 0; 1 4 -1; 0 -1 2] [x;y;z]


Homework Equations





The Attempt at a Solution


I'm fairly certain this is the second derivative of a taylor series expansion so 3rd term. So the matrix [3 1 0; 1 4 -1; 0 -1 2] is the Hessian. What I do not know now is how to get the maximum/minimum/neither or the critical points from the hessian.
 
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  • #2
The critical points are such that all the partial derivatives are 0
 
  • #3
So does that mean where 3x+y=0, x+4y-z=0, and -y+2z=0?
 
  • #4
sdevoe said:
So does that mean where 3x+y=0, x+4y-z=0, and -y+2z=0?

yes you are right
 
  • #5
Confirming that I have to solve it as a system of equations?
 
  • #6
sdevoe said:
Confirming that I have to solve it as a system of equations?

Yes, of course. That is exactly how critical points are found, in general.

RGV
 
  • #7
I will get that all the values are equal to zero if I solve that?
 
  • #8
sdevoe said:
I will get that all the values are equal to zero if I solve that?

Try it and see.

RGV
 

1. What is a Hessian matrix?

The Hessian matrix is a square matrix of second-order partial derivatives of a multivariate function. It is commonly used in optimization and approximation methods in mathematics and physics.

2. How is the Hessian matrix used in Taylor expansion?

In Taylor expansion, the Hessian matrix is used to calculate the second-order approximation of a multivariate function. It is used to estimate the curvature and shape of the function at a specific point.

3. What is the significance of the Hessian matrix in optimization?

The Hessian matrix plays a crucial role in optimization by providing information about the local maxima and minima of a function. It is used to determine if a critical point is a maximum, minimum, or saddle point.

4. How is the Hessian matrix calculated?

The Hessian matrix is calculated by taking the second derivatives of a multivariate function and arranging them into a square matrix. It can also be calculated using matrix operations such as the Jacobian matrix and the Laplacian operator.

5. What are some applications of the Hessian matrix?

The Hessian matrix has various applications in fields such as economics, physics, and engineering. It is used in optimization problems, machine learning algorithms, and in the study of critical points and stability of dynamical systems.

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