Function two wariables - hessian matrix is 0

In summary, the hessian matrix of a function can be used to classify stationary points as extreme or non-extreme. This is done by calculating the eigenvalues of the hessian matrix and determining if they are all positive, all negative, or a mix of positive and negative. If there is at least one null eigenvalue, further calculations may be necessary to determine the type of stationary point. This process is described in most calculus textbooks.
  • #1
player1_1_1
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Homework Statement


what can I do if I have hessian = 0? ex. function
[tex]f(x,y)=x^2+y^4[/tex]
hessian is 0, what now? this is simply but what can i do in more complicated functions?
 
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  • #2
The hessian matrix of f isn't zero in all entries, if you do the math you can easily see that.

However, i don't understand your question. I'm guessing you wish to classify the stationary points of f?
 
  • #3
sorry, I didnt explain it good:) i need to know how can I classify stationary to extreme points or non-ekstreme depending on hessian?
 
  • #4
player1_1_1 said:
sorry, I didnt explain it good:) i need to know how can I classify stationary to extreme points or non-ekstreme depending on hessian?

The demonstration for why these conditions are the conditions that allow you to classify stationary/non stationary points is in every calculus book that approaches this subject.

After you get the hessian matrix, you have to calculate it's eigen values, if all of them are positive then you have a minimum, if all are negative, then it is a maximum, if some are negative and some are positive then you don't have any of the previous one's, in this case you might have a saddle point (imagine a horse saddle-like surface).

In the case that you at least one null eigen value then, to find out what kind of stationary point it is, you will most likely have to calculate the taylor expansion of the function and see how the function varies with that approximation.
 

1. What is the Hessian matrix in relation to the function of two variables?

The Hessian matrix is a square matrix of second-order partial derivatives of a function of two variables. It is commonly used in multivariate calculus to determine the critical points of a function and classify them as maxima, minima, or saddle points.

2. Why is it important for the Hessian matrix to have a determinant of 0?

The determinant of the Hessian matrix being 0 indicates that the function has a critical point, but it does not provide information about the nature of that critical point. This can lead to ambiguity in determining the optimal solution to a problem.

3. Can a function have a Hessian matrix of 0 at a non-critical point?

Yes, it is possible for a function to have a Hessian matrix of 0 at a non-critical point, but this is not a common occurrence. In most cases, a Hessian matrix of 0 indicates a critical point.

4. How does the Hessian matrix relate to the second derivative test?

The second derivative test states that if the second partial derivatives of a function are continuous and the Hessian matrix is positive definite at a critical point, then that critical point is a local minimum. Similarly, if the Hessian matrix is negative definite, the critical point is a local maximum. If the Hessian matrix is indefinite, the critical point is a saddle point.

5. Is the Hessian matrix always a square matrix?

Yes, the Hessian matrix is always a square matrix since it is composed of second-order partial derivatives, which are taken with respect to two variables. The size of the matrix is determined by the number of variables in the function.

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