Solving Eigenvalues of Hessian Matrix

In summary, the conversation is about finding the eigenvalues of a Hessian matrix. The Hessian matrix given in the conversation has a numerical error, which should be corrected before proceeding. To find the eigenvalues, one needs to solve for the variable a, as they would normally. The final Hessian matrix should have the values -6, 5, 5, and -14 in its rows and columns. One may also need to plug in specific values for x and y to find the eigenvalues.
  • #1
Firepanda
430
0
g(x,y) = x^3 - 3x^2 + 5xy -7y^2

Hessian Matrix =

6x-6******5

5********-7

Now I have to find the eigenvalues of this matrix, so I end up with the equation (where a = lambda)

(6x - 6 - a)(-7 - a) - 25 = 0

Multiplying out I get:

a^2 - 6xa + 13a - 42x + 17 = 0

How am I supposed to solve for a? Usually I just use the quadratic formula for my eigenvalues..

Should I take a third row/ column for my hessian matrix?
 
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  • #2
First off, you have a numerical error in your Hessian. Double check your derivation.

To find the eigenvalues, just solve for a as you would normally (i.e., the eigenvalues will be functions of x).
 
  • #3
http://img229.imageshack.us/img229/2286/hesssn5.jpg

That's the whole question, I assume I'm doing something wrong because my roots of the quadratic for lamba is very complicated.

And yes thankyou I changed my -7 value in my hessian for -14
 
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  • #4
So, what is H(0,0)?
 
  • #5
D H said:
So, what is H(0,0)?

I've no idea :D this is the 1st hessian question I've ever done, i also previosuly made a thread in the same section about what this notation meant, because I'm not too sure.

Do I just plug in x= 0 and y=0?

So my hessian would be:

-6*****5

5*****-14
 

1. What is a Hessian matrix?

A Hessian matrix is a square matrix containing second-order partial derivatives of a multivariable function. It is used to determine the curvature of a function's graph and is an important tool in optimization and machine learning algorithms.

2. Why is solving eigenvalues of a Hessian matrix important?

Solving eigenvalues of a Hessian matrix allows us to determine the critical points of a function, which are the points where the gradient is equal to zero. This is important for optimization problems as it helps us identify the maximum and minimum points.

3. How do you solve eigenvalues of a Hessian matrix?

To solve eigenvalues of a Hessian matrix, we first find the second-order partial derivatives of the function. Then, we construct the Hessian matrix and use an algorithm such as the Jacobi or Gauss-Seidel method to compute the eigenvalues.

4. What is the significance of the eigenvalues of a Hessian matrix?

The eigenvalues of a Hessian matrix can tell us about the curvature of the function at a particular point. A positive eigenvalue indicates a minimum point, a negative eigenvalue indicates a maximum point, and a zero eigenvalue indicates a saddle point.

5. Can the eigenvalues of a Hessian matrix be used for optimization?

Yes, the eigenvalues of a Hessian matrix can be used to optimize functions. By identifying the critical points, we can determine the direction of steepest ascent or descent, which can guide us towards the optimal solution.

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