How Do You Solve for the Smallest Eigenvalue in a Quadratic Equation?

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In summary, the person is seeking help with setting up the auxiliary equation and solving for λ in an equation involving m. They have obtained the solution -2 ± sqrt(5λ-1)i but are unsure of what to do next. Based on the given information, the person is advised to consider three cases for λ and to focus on finding the smallest eigenvalue, which is likely to be one of the roots of the characteristic equation.
  • #1
amninder15
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Can someone help me with this question?

I know we have to set up the auxiliary equation and then solve for λ but for some reason I am not getting the right answer.

My equation is:

m^2 + 4m + (5λ + 3) = 0

then we get -2 ± sqrt(5λ-1)i

Now can somebody explain what I have to do after this?
 

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  • #2
amninder15 said:
Can someone help me with this question?

I know we have to set up the auxiliary equation and then solve for λ but for some reason I am not getting the right answer.

My equation is:

m^2 + 4m + (5λ + 3) = 0

then we get -2 ± sqrt(5λ-1)i

Now can somebody explain what I have to do after this?

You are assuming that 1 - 5λ < 0 or IOW, that λ > 1/5. Are you given any information about λ?

I would break this into three cases:
λ < 1/5, implying two real values of m.
λ = 1/5, implying one real and repeated value of m; namely m = -2.
λ > 1/5, implying two complex values of m.

Since the question asks for the smallest eigenvalue, I take it that they are referring to the roots of the characteristic equation, or m. Also, since complex numbers aren't ordered (you can't compare them with < or >), I guess that limits things to the first two cases above.
 

Related to How Do You Solve for the Smallest Eigenvalue in a Quadratic Equation?

1. What is the smallest eigenvalue and why is it important?

The smallest eigenvalue is the smallest non-zero eigenvalue of a matrix. Eigenvalues are important because they provide insight into the behavior and properties of a matrix, such as stability, convergence, and rate of change.

2. How is the smallest eigenvalue determined?

The smallest eigenvalue can be determined through different methods, such as the power iteration method or the Rayleigh quotient iteration method. These methods involve iteratively solving eigenvalue equations until the desired smallest eigenvalue is found.

3. What are some applications of finding the smallest eigenvalue?

Finding the smallest eigenvalue is useful in various fields such as economics, engineering, and physics. It can be used to analyze the stability of a system, predict the behavior of a system, and solve optimization problems.

4. What are some challenges in solving for the smallest eigenvalue?

One challenge is that the smallest eigenvalue may not be unique, meaning there could be multiple eigenvalues with the same magnitude. Another challenge is that the computation of eigenvalues can be time-consuming and computationally intensive, especially for large matrices.

5. Are there any techniques to improve the accuracy of the smallest eigenvalue?

Yes, there are techniques such as using preconditioning methods, which involve transforming the original matrix into a more suitable form to improve the convergence and accuracy of eigenvalue computations. Additionally, using higher precision arithmetic or parallel computing can also help improve accuracy.

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