Eigenvalues & Eigenfunctions: Exploring Physical Significance

In summary,Eigenvalues and eigenvectors come from the study of linear algebra and are solutions to the equation Av = λv. They have a physical significance in certain applications and can be represented by a diagonal matrix in the case of a complete set of eigenvectors. The physical significance of eigenvalues and eigenvectors depends on the specific physics application and model being used.
  • #1
Shubha.Sagar
1
0
what is a eigen value and eigen function? i have read a lot abt it...i understand the math behind it.. what is its physical significance of it?
 
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  • #2
Have you studied Linear Algebra? That's really where it comes from. If A is a linear operator on some vector space, the "eigenvalue problem" is: [itex]Av= \lambda v[/itex]. That has, of course, the "trivial" solution v= 0. For some values of [itex]\lambda[/itex], called "eigenvalues", there are other, non-trivial, solutions- in fact the set of all solutions, in that case, is a sub-space. Those non-trivial solutions are the "eigenvectors".

Of course, in the finite dimensional case, after we have chosen a basis, we can write a linear operator as a matrix, with each column showing what the operator takes each basis vector to (written in terms of that basis). If we choose an eigenvector, corresponding to eigen value [itex]\lambda[/itex] as the "nth" basis vector, the nth column consists of "0"s except for the value [itex]\lambda[/itex] in the nth row. In particular, if we can find a "complete set of eigenvectors"- that is, a basis consisting entirely of eigenvectors- which we always can in the important "self adjoint operator" case, then the matrix representing the linear operator in that basis is diagonal- the simplest kind of matrix.

If the vector space is a "function space", typically, infinite dimensional, we cannot write the operator as a matrix but still, using as many eigenvectors (eigenfunctions in this case) as basis vectors, and especially in the "self adjoint" case, using eigenfunctions for all basis functions, allows us to write the operator in a particularly simple form.

As for the "physical significance"- like any question about the physical significance of a mathematical concept, that depends entirely upon the particular physics application and which "model" you are using. That would be a question more appropriate to a physics forum that a mathematics forum.
 
  • #3

What are eigenvalues and eigenfunctions?

Eigenvalues and eigenfunctions are mathematical concepts that are used to describe certain properties of a system or object. Eigenvalues are the values that, when multiplied by an eigenvector, result in a scaled version of the same eigenvector. Eigenfunctions are the corresponding functions that, when operated on by a linear operator, result in a scaled version of the original function.

What is the physical significance of eigenvalues and eigenfunctions?

Eigenvalues and eigenfunctions have important physical significance in many fields of science and engineering. In quantum mechanics, they represent the allowed energy levels of a system. In structural engineering, they represent the natural frequencies of a structure. In data analysis, they can be used to identify patterns and relationships in a dataset.

How are eigenvalues and eigenfunctions calculated?

The process of calculating eigenvalues and eigenfunctions involves finding the values and functions that satisfy a specific mathematical equation, known as the eigenvalue problem. This can be done using various techniques such as diagonalization, power iteration, or the QR algorithm. The exact method used depends on the specific problem and the properties of the system.

What are some real-life applications of eigenvalues and eigenfunctions?

Eigenvalues and eigenfunctions are used in a wide range of practical applications. In physics, they are used to solve problems in quantum mechanics, electromagnetics, and fluid dynamics. In engineering, they are used for structural analysis and control systems. In data analysis, they are used for dimensionality reduction and feature extraction. They also have applications in finance, economics, and image processing.

What is the relationship between eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are closely related. Eigenvectors are the vectors that correspond to the eigenvalues of a system or object. They represent the directions in which a system remains unchanged when operated on by a linear operator. The eigenvalues determine the scaling factor for each eigenvector. In other words, the eigenvalues and eigenvectors are like two sides of the same coin, with each one providing important information about the system.

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