Hessian matrix, maxima and minima criteria

In summary: Therefore, the only critical point (0,0) is a saddle point, as it is not a local extremum. In summary, the conversation discusses doubts about finding the maxima and minima of the function f(x,y)=x \sin y. The criteria for determining concavity fails when the second derivative f_{xx}=0, but we can still use the second derivative f_{yy} to determine the concavity. The first derivative f'(x,y) can also be used to find the local extrema of the function. The only critical point (0,0) is a saddle point, as it is not a local extremum.
  • #1
Telemachus
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Homework Statement


Hi there. I've got some doubts about the maxima and minima on this function: [tex]f(x,y)=x \sin y[/tex]. I've looked for critical points, and there's only one at (0,0). The thing is that when I've evaluate the second derivatives I've found that [tex]f_{xx}=0[/tex], then I have not a defined criteria on this case, cause the criteria that I have defines the concavity for [tex]f_{xx}>0[/tex] and [tex]f_{xx}<0[/tex]. I don't know what happens when [tex]f_{xx}=0[/tex]. I think the criteria fails, but I don't actually know what to do.

Bye there!
 
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  • #2
Homework Equations The second derivatives of f(x,y) are f_{xx}=0 and f_{yy}=x \cos y. The Attempt at a Solution Since the second derivative f_{xx}=0, we can't use the usual criteria for finding the concavity of the function. However, we can still use the second derivative f_{yy}. We know that f_{yy}=x \cos y, so if x > 0, then f_{yy} > 0, indicating that the function is concave up. Similarly, if x < 0, then f_{yy} < 0, indicating that the function is concave down. We can also use the first derivative f'(x,y)=\sin y to determine the local extrema of the function. Since \sin y is periodic, it will have local maxima and minima at the points where its derivative is zero. Thus, the local extrema of f(x,y) occur when \sin y = 0, which means that the local maxima occur at (x,y)=(x, \pi/2 + 2n\pi) and the local minima occur at (x,y)=(x, -\pi/2 + 2n\pi).
 

FAQ: Hessian matrix, maxima and minima criteria

1. What is a Hessian matrix?

A Hessian matrix is a square matrix of second-order partial derivatives of a multivariable function. It is used to determine the critical points of a function, including maxima and minima.

2. How is the Hessian matrix used to find maxima and minima?

The Hessian matrix is used by calculating its eigenvalues. If all eigenvalues are positive, the point is a local minimum. If all eigenvalues are negative, the point is a local maximum. If there are both positive and negative eigenvalues, the point is a saddle point.

3. What is the second derivative test?

The second derivative test is a method for determining whether a critical point of a function is a maximum, minimum, or saddle point. It involves using the Hessian matrix to calculate the eigenvalues and determine the nature of the critical point.

4. Can the Hessian matrix be used for functions with multiple variables?

Yes, the Hessian matrix can be used for functions with multiple variables. It is a useful tool for finding critical points and determining the nature of those points in multivariable functions.

5. Are there any limitations to using the Hessian matrix to find maxima and minima?

One limitation is that the Hessian matrix only provides information about local maxima and minima, not global ones. Additionally, it may not always be possible to calculate the eigenvalues of the Hessian matrix, making it difficult to determine the nature of a critical point.

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