Eigenvalues, Eigenspaces, and Basis

In summary, this conversation discusses finding the eigen values and eigenspaces of a given matrix, as well as determining a basis for each eigenspace. The concept of a basis is defined as a set of linearly independent vectors that can be used to write all other vectors in the set as linear combinations of the basis vectors. It is important for the basis to also "span" the vector space. In this case, the eigenspace E_1 can be generated by the eigenvector e_1, making it a basis for E_1.
  • #1
Saladsamurai
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7

Homework Statement



Find the eigen values, eigenspaces of the following matrix and also determine a basis for each eigen space for A = [1, 2; 3, 4]

Homework Equations



[itex]\det(\mathbf{A} - \lambda\mathbf{I}) = 0[/itex]

The Attempt at a Solution



OK, so I found the eigenvalues and eigenspaces just fine. For eigen values I found [itex]\lambda_{1,2} = -0.372, 5.372}[/itex] which matches the answer in text. I also found that e1 = [-1.457, 1]T and e2 = [0.4575, 1]T which is also correct.

It is this "basis" thing that I am not understanding. Please keep in mind this is an engineering advanced math methods course, so though I do know about matrix operations, I am by no means a linear algebra wizard over here.

Upon looking up the definition of a basis, it seems that it is just a set of linearly independent (LI) vectors that can be used to write all of the other vectors in that particular set in terms of (i.e. as linear combinations of). OK great. So a set {u1, u2} in a vector space S is a basis for S iff u1 and u2 are LI. And apparently the set needs to "span S" too (still working on what that means).

So I guess what my big question is, is what is my S for which I am trying to find basises (spelling?) for? How do I start this?
 
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  • #2
So, if I understand correctly, there is an eigenvector [tex]e_1[/tex] associated with [tex]\lambda_1[/tex].
You've only found 1 vector associated with [tex]\lambda_1[/tex]. There are more eigenvectors (which together make up the eigenspace), namely all multiples of the vector. Thus the eigenspace is

[tex]E_1=\{\alpha e_1~\vert~\alpha\in \mathbb{R}\}[/tex]

A basis for this space is just a linearly independent set which generates [tex]E_1[/tex]. Right now, you've already found a vector which generates [tex]E_1[/tex], that is: [tex]e_1[/tex] generates the space. Thus this vector is a basis: the basis of [tex]E_1[/tex] is thus [tex]\{e_1\}[/tex].
 

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe how a linear transformation affects a vector. An eigenvalue is a scalar value that represents how much the eigenvector is stretched or compressed by the transformation. An eigenvector is a vector that remains in the same direction after being transformed.

Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they allow us to analyze and understand linear transformations. They can help us identify patterns and characteristics of a transformation, such as its scale and direction. They also have many applications in fields such as physics, engineering, and computer graphics.

What is an eigenspace?

An eigenspace is the set of all eigenvectors that correspond to a specific eigenvalue. In other words, it is the subspace of the original vector space that is preserved by the linear transformation. The dimension of an eigenspace is equal to the multiplicity of its corresponding eigenvalue.

What is a basis?

A basis is a set of linearly independent vectors that can be used to represent any vector in a given vector space. It is a fundamental concept in linear algebra and is often used to simplify computations and understand the structure of a vector space.

How are eigenvalues, eigenvectors, and basis related?

Eigenvalues and eigenvectors are closely related to the concept of basis. The eigenvectors of a linear transformation form a basis for the eigenspace, and the eigenvalues represent the scaling factor of each eigenvector in that basis. Furthermore, eigenvectors can be used to form a basis for the entire vector space, allowing us to represent any vector in the space as a linear combination of the eigenvectors.

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