Eigen functions & eigen vectors

In summary, in Quantum mechanics, we deal with eigen value equations which involve terms like eigen values, eigen vectors, and eigen functions. These terms are used interchangeably and refer to the same concept of quantities that are operated on by an operator and result in the same quantity multiplied by a constant. The terms "eigenket" and "eigenfunction" are used in different formalisms, but they have the same mathematical meaning. The term "eigenstate" is also used interchangeably, although it is technically a representation of an equivalence class of vectors.
  • #1
Amith2006
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In Quantum mechanics, we frequently deal with eigen value equations. When we speak of eigen value equations, we come across terms like eigen values,eigen vectors,eigen functions etc. When an operator is operated on certain quantities we get the same quantity multiplied by a constant. These quantities are interchangeably referred as eigen vectors and eigen functions. But do they mean the same? Is it something like, we call it as eigenfunctions in Schrodinger formalism and eigenvectors or eigenkets in Heisenberg formalism or is there a ma thematical difference between the 2?
 
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They are the same. It would be weird to use "eigenket" when you're not using bra-ket notation, and it would be weird to use "eigenfunction" if you're talking about a vector that isn't actually a function, but other than that they're the same. The term "eigenstate" is also used interchangeably with the others. That's actually a little bit weird since a "state" is represented by an equivalence class of vectors. (Two vectors are equivalent if one of them is a complex number times the other).
 
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  • #3


Eigen functions and eigen vectors are closely related concepts in quantum mechanics. They both refer to the properties of an operator acting on a particular state or system. However, there is a slight difference between the two terms.

Eigen functions are mathematical functions that represent the state of a system in quantum mechanics. They are solutions to the eigenvalue equation, which is a mathematical expression that describes the relationship between the operator, the state, and the eigenvalue. In other words, eigen functions are the possible states of a system that satisfy the eigenvalue equation.

On the other hand, eigen vectors are mathematical objects that represent the state of a system in a particular basis. They are solutions to the eigenvalue equation, just like eigen functions, but they are written in terms of a basis set. In quantum mechanics, the basis set is often referred to as eigenkets.

So, while eigen functions can be seen as the general form of a state, eigen vectors are specific representations of that state in a particular basis. In other words, eigen functions are more abstract and general, while eigen vectors are more concrete and specific.

However, there is a mathematical equivalence between eigen functions and eigen vectors. In fact, the eigen functions can be expanded in terms of eigen vectors, and vice versa. This means that the two terms are essentially interchangeable, and the choice of which one to use may depend on the context or the formalism being used.

In summary, eigen functions and eigen vectors are closely related concepts in quantum mechanics, but they have slight differences in their mathematical representations. Both terms refer to the properties of an operator acting on a state, and they can be used interchangeably in most cases.
 

1. What are eigenfunctions and eigenvectors?

Eigenfunctions and eigenvectors are mathematical concepts used in linear algebra to describe the relationship between a linear transformation and its corresponding scalar values. Eigenfunctions are functions that, when multiplied by a constant, remain unchanged except for a possible scaling factor. Eigenvectors are vectors that, when multiplied by a transformation matrix, result in a scalar multiple of the original vector.

2. How are eigenfunctions and eigenvectors related?

Eigenfunctions and eigenvectors are closely related because an eigenfunction can be thought of as a special type of eigenvector. In other words, an eigenfunction is a vector in a function space that corresponds to an eigenvalue, while an eigenvector is a vector in a vector space that corresponds to an eigenvalue.

3. What is the significance of eigenfunctions and eigenvectors?

Eigenfunctions and eigenvectors are important in many areas of mathematics and physics, as they provide a way to simplify and analyze complex systems. They are particularly useful in solving differential equations and in understanding the behavior of quantum mechanical systems.

4. How do eigenfunctions and eigenvectors relate to matrix operations?

Eigenfunctions and eigenvectors are used to diagonalize a matrix, which is the process of finding a simpler form of a matrix that still represents the same linear transformation. This allows for easier calculations and analysis of the matrix. Eigenvectors also play a key role in determining the principal components of a matrix.

5. Can there be multiple eigenfunctions and eigenvectors for a given matrix?

Yes, a matrix can have multiple eigenfunctions and eigenvectors, each corresponding to a different eigenvalue. These eigenfunctions and eigenvectors are considered to be orthogonal, meaning they are perpendicular to each other and form a basis for the vector space. This allows for a variety of different transformations to be represented by a single matrix.

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