Question about an eigenvalue problem: range space

In summary, Axler's theorem states that for a linear operator on a complex finite dimensional vector space, there exists a basis such that the matrix representation of the operator is upper triangular. In the proof, the range space is defined as the set of all vectors in the codomain of the operator that have a solution when operated on by the operator. This range space is also a subspace. The proof also shows that the range space is invariant under the operator, and this is proved using the identity ##Tu = Tu##. The proof does not assume that the vectors in the range space are eigenvectors of the operator.
  • #1
bluesky314
1
0
A theorem from Axler's Linear Algebra Done Right says that if 𝑇 is a linear operator on a complex finite dimensional vector space 𝑉, then there exists a basis 𝐵 for 𝑉 such that the matrix of 𝑇 with respect to the basis 𝐵 is upper triangular.
In the proof, he defines U=range(T-𝜆I) (as we have proved atleast one eigenvalue must exist)and says that this is invariant under T.
First I want understand what the range space is here. Suppose we are in 2D and I choose some basis consisting on an eigenvector(ev) and another free vector(v): ( ev, v) Now this v can be anything not necessarily an eigenvector. So there are many possibilities. Are all contained in U? U can have dimension 1 but all these possibilities are not linearly independent so what exactly is U? Is it a set of all vectors not in the span of ev? Does this not conflict with U having dimension 1?
He then proves this by saying if u belongs to U then Tu=(T-𝜆I)u + Tu and both (T-𝜆I)u and Tu belong to U. But if I had chosen a non-eigenvalue for my basis, then Tu does not scale it so it is mapped to some other span. Therefore I'm guessing U is a set.
Any other comments about (T-𝜆I)v would be appreciated. How to think of it in the context of our original T? (I know the null space is the eigenvector)
 
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  • #2
bluesky314 said:
But if I had chosen a non-eigenvalue for my basis, then Tu does not scale it so it is mapped to some other span.

The range of a linear operator ##L## from a vector space ##V## to a vector space ##W## is the set ##\{u\in W:## such that ##Lx =u## has a solution ##x\in V\}##. The range is also a subspace of ##W##.

Simplifying the right hand side of the equation ##Tu = (T - \lambda)u + \lambda u## shows it is equivalent to the identity ##Tu = Tu##.

The proof does not assume that ##u## is an eigenvector of ##T## or of ##(T - \lambda)##, so your objection is unclear. The assumption ##u \in U## is equivalent to assuming the existence of ##x## such that ##(T-\lambda)x = u##. I don't know whether you could that expression to formulate a different proof that ##U## is invariant under ##T##.
 

1. What is an eigenvalue problem?

An eigenvalue problem is a mathematical problem that involves finding the values (known as eigenvalues) and corresponding vectors (known as eigenvectors) that satisfy a specific equation. This equation is typically in the form of a matrix equation and is used to solve a variety of problems in physics, engineering, and other fields.

2. What is the range space of an eigenvalue problem?

The range space of an eigenvalue problem is the set of all possible values that the eigenvectors can take on. In other words, it is the span of all the eigenvectors associated with the eigenvalues of the problem. This range space is important because it represents the possible solutions to the problem.

3. How is the range space related to the eigenvalues of an eigenvalue problem?

The range space is directly related to the eigenvalues of an eigenvalue problem. Each eigenvalue corresponds to a specific eigenvector, and the set of all eigenvectors forms the range space. The eigenvalues determine the scaling factor for each eigenvector in the range space.

4. How do you find the range space of an eigenvalue problem?

To find the range space of an eigenvalue problem, you first need to find the eigenvalues and eigenvectors. This can be done by solving the characteristic equation of the matrix equation. Once you have the eigenvalues and eigenvectors, you can use them to form the range space by taking all possible linear combinations of the eigenvectors.

5. Why is the range space important in solving an eigenvalue problem?

The range space is important in solving an eigenvalue problem because it represents all the possible solutions to the problem. By finding the range space, you can determine the full set of solutions and understand the behavior of the system described by the eigenvalue problem. Additionally, the range space can help identify any special cases or patterns in the solutions that may be useful in solving the problem.

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