Much Ado about Eigenvalues and their Vectors

Eigenvalues and Eigenvectors:

• What is an eigenvector?
•  Are vectors which are fixed in direction under a given linear transformation. 
• What is an eigenvalue?
• Is the scaling factor of these eigenvectors.

⭐ The great thing about eigenvectors and eigenvalues is that it allows simple linear equations (Ax=b),which are referred to as steady state problems, to be become dynamic problems(includes rate of change over periods of time) that answer a variety of questions. 

Rules:

1. A scalar lambda(λ) is an eigenvalue of a square matrix A if there exists a nontrivial solution for x of Ax=λx  . The resulting nontrivial solution of x is called the eigenvector; which corresponds to the eigenvalue (λ)
2. An eigenvalue is able to be zero but an eigenvector is not able to be equivalent to zero.
3. A n*n matrix A is found to be invertible only if zero is not an eigenvalue of A
1. Suppose eigenvalue of A=0 Then there must be a nontrivial solution for the vector x...
• Meaning:
• Ax=0*x=0
1. This would imply that A is in fact not invertible
4. Standard form equation; since Ax=λx is not in a familiar form seeing as the unknown 'x' is on both sides of the equation, the identity matrix is used to rectify the form and allow 'x' to be solved for more easily. 
• Ax=λx
• Ax-λx=0
• Ax-λIx=0
• (A-λI)x=0
5. Eigenspace of the square matrix A corresponds to the eigenvalue of A and all of the corresponding eigenvectors.
6. Eigenspace of λ is the null space of matrix A-λI; which then makes it a subspace of Rⁿ

⭐Eigenvector= (A-λI)x=0               ⭐Eigenvalue=(A-λI)x=0

Example:

Given eigenvalue to find eigenvector

Using eigenvectors to find eigenvalues

Practice:

Exercise EE.C11 Chris Black

Find the characteristic polynomial of the matrix $A=\left[\begin{array}{ccc}3& 2& 1\\ 0& 1& 1\\ 1& 2& 0\end{array}\right]$.

Solution

Exercise EE.C12 Chris Black

Find the characteristic polynomial of the matrix $A=\left[\begin{array}{cccc}1& 2& 1& 0\\ 1& 0& 1& 0\\ 2& 1& 1& 0\\ 3& 1& 0& 1\end{array}\right]$.

Solution

Exercise EE.C21 Robert Beezer

The matrix $A$ below has $\lambda =2$ as an eigenvalue. Find the geometric multiplicity of $\lambda =2$ using your calculator only for row-reducing matrices.$$A=\left[\begin{array}{cccc}18& -15& 33& -15\\ -4& 8& -6& 6\\ -9& 9& -16& 9\\ 5& -6& 9& -4\end{array}\right]$$

Solution

Exercise EE.C22 Robert Beezer

Without using a calculator, find the eigenvalues of the matrix $B$.$$B=\left[\begin{array}{cc}2& -1\\ 1& 1\end{array}\right]$$

Solution

Exercise EE.C23 Chris Black

Find the eigenvalues, eigenspaces, algebraic and geometric multiplicities for $A=\left[\begin{array}{cc}1& 1\\ 1& 1\end{array}\right].$

Beezer, R. (n.d.). Eigenvalues and eigenvectors. Retrieved October 27, 2017, from http://linear.ups.edu/html/section-EE.html  Problem set and solutions

Black, C. (n.d.). Eigenvalues and eigenvectors. Retrieved October 27, 2017, from http://linear.ups.edu/html/section-EE.html  Problem set and solutions

Useful applications:

Fields that benefit from eigenvalues:

• Physics
• Quantum mechanics
• Engineering
• Biology
• Ecology-i.e. Leslie model
• Describes the growth of populations (and their projected age distribution), in which a population is closed to migration, growing in an unlimited environment, and where only one sex, usually the female, is considered. The dominant eigenvalue of ${\displaystyle \mathbf {L} }$( Leslie matrix), gives the population's asymptotic growth rate (growth rate at the stable age distribution). The corresponding eigenvector provides the stable age distribution, the proportion of individuals of each age within the population. Once the stable age distribution has been reached, a population undergoes exponential growth at rate ${\displaystyle \lambda }$.
• Microbiology
• Geology and glaciology
• Face recognition software:
• "In image processing, processed images of faces can be seen as vectors whose components are the brightnesses of each pixel. The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated with a large set of normalized pictures of faces are called eigenfaces; this is an example of principal component analysis. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigenfaces provide a means of applying data compression to faces for identification purposes. Research related to eigen vision systems determining hand gestures has also been made".
1. (n.d.) Eigenface. Retrieved 10.29.2017. from https://en.wikipedia.org/wiki/Eigenface[45]

Helpful resources:

Beautiful visual representation of eigenvectors and values

Eigenvalues simplified

The skinny on eigenvalues and their vectors

All you need to know about eigenvalues and their applications

Eigenvalues and physics

Learn the ins and outs of eigenvalues and eigenvectors