Why were eigenvalues and eigenvectors defined?

In summary: Eigenvalues are solutions to the equation which correspond to the eigenvectors.The importance of eigenvalues and eigenvectors is that they allow us to find solutions which are linearly independent. This is important because it means that we can solve the equation for multiple solutions without having to worry about interference between them.Eigenvalues and eigenvectors are also important in the theory of quadratic forms and differential equations. In these cases, they play a similar role as the principal axes in the case of the beam. They define a basis from which all other solutions can be found.
  • #1
Tosh5457
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I know some of their applications, but I wanted to know how they first appeared. Why were eigenvalues and eigenvectors needed?
 
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  • #3
micromass said:
Wiki knows all: http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
Check under section "history"

I tried to understand that before asking here, but I didn't...

Eigenvalues are often introduced in the context of linear algebra or matrix theory. Historically, however, they arose in the study of quadratic forms and differential equations.
Euler studied the rotational motion of a rigid body and discovered the importance of the principal axes. Lagrange realized that the principal axes are the eigenvectors of the inertia matrix.[11] In the early 19th century, Cauchy saw how their work could be used to classify the quadric surfaces, and generalized it to arbitrary dimensions.[12] Cauchy also coined the term racine caractéristique (characteristic root) for what is now called eigenvalue; his term survives in characteristic equation.[13]

How exactly did they arise from the study of quadratic forms and differential equations?
 
  • #4
As a simple example of why principal axes are important, consider bending of a cantilever beam with a rectangular cross section.

If you apply a force in the direction of one of the principal axes, the beam bends in the same direction as the force. The stiffness (Force / displacement) will be different for the two principal axes, depending on the relative width and depth of the beam (I = bd3/12 in one direction and b3d/12 in the other.)

If you apply a force at an angle to the principal directions, the beam does NOT bend in the same direction as the force. You can find the direction by resolving the force into components in the principal directions, finding the corresponding components of displacement, and combining them.

How all that relates to the eigenvalues and vectors of the 2x2 inertia matrix for the cross section of the beam should be fairly obvious.
 
  • #5
Tosh5457 said:
I know some of their applications, but I wanted to know how they first appeared. Why were eigenvalues and eigenvectors needed?

The Wikipedia article is worth looking at.

In the theory of linear ODEs, eigen vectors define a basis from which all other solutions are linear combinations.
 

1. What is the purpose of defining eigenvalues and eigenvectors?

The definition of eigenvalues and eigenvectors is important in linear algebra as they provide a way to understand and analyze the behavior of linear transformations. They also have numerous applications in fields such as physics, engineering, and computer science.

2. How are eigenvalues and eigenvectors used in data analysis?

In data analysis, eigenvalues and eigenvectors are used to reduce the dimensionality of data sets and identify important patterns and relationships among variables. This allows for easier interpretation and visualization of complex data.

3. Why were eigenvalues and eigenvectors given specific names?

The terms "eigenvalue" and "eigenvector" were chosen by the mathematician David Hilbert in the early 20th century. "Eigen" is a German word meaning "characteristic" or "inherent," and these terms accurately describe the unique properties of these mathematical concepts.

4. How do eigenvalues and eigenvectors relate to linear independence?

Eigenvalues and eigenvectors are closely related to linear independence. In fact, eigenvectors are linearly independent from each other and can be used to form a basis for the vector space in which they exist. This makes them useful in solving systems of linear equations.

5. Can eigenvalues and eigenvectors have complex values?

Yes, eigenvalues and eigenvectors can have complex values. This is especially relevant in quantum mechanics, where complex numbers are used to represent the state of a physical system. Complex eigenvalues and eigenvectors can also provide more information about the behavior of a linear transformation.

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