Action of a linear operator on vectors

In summary, the vectors $$| a \rangle, \mathbf{A} | a \rangle, \mathbf{A}^2 | a \rangle, \ldots, \mathbf{A}^N | a \rangle$$ are linearly dependent, and there exists a subset of these vectors that can be expressed as ##\mathbf{A}^M | a \rangle = \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^k | a \rangle## with at least one nonzero ##\alpha_k##. By multiplying with appropriate powers of ##\mathbf{A}##, we can show that
  • #1
DrClaude
Mentor
8,383
5,474
Not really a homework problem, just doing some self-studying.

Homework Statement


Let ##| a \rangle## by any vector in an ##N##-dimensional vector space ##\mathcal{V}##, and ##\mathbf{A}## a linear operator on ##\mathcal{V}##. The vectors
$$
| a \rangle, \mathbf{A} | a \rangle, \mathbf{A}^2 | a \rangle, \ldots, \mathbf{A}^N | a \rangle
$$
are linearly dependent. Let ##\mathcal{M} \equiv \text{Span}\{ \mathbf{A}^k | a \rangle\}_{k=0}^N##. Show that ##\mathcal{M}## has the property that for any vector ##| x \rangle \in \mathcal{M}##, the vector ##\mathbf{A} | x \rangle## also belongs to ##\mathcal{M}##.

Homework Equations





The Attempt at a Solution


Since the above vectors span ##\mathcal{M} ##, I can always find ##\{c_k\}## such that I can write
$$
| x \rangle = \sum_{k=0}^N c_k \mathbf{A}^k | a \rangle
$$
Now, if ##c_N = 0##, and since ##\mathbf{A}## is a linear operator, I have
$$
\begin{array}{rl}
\mathbf{A} | x \rangle & = \mathbf{A} \sum_{k=0}^{N-1} c_k \mathbf{A}^k | a \rangle \\
& = \sum_{k=0}^{N-1} c_{k} \mathbf{A}^{k+1} | a \rangle
\end{array}
$$
which, by definition, is in ##\mathcal{M}##.

But what if ##c_N \neq 0##? I need to show that ##\mathbf{A}^{N+1} | a \rangle## is in ##\mathcal{M}##. I'm tempted to use the fact that the vectors are linearly dependent to state that I can write
$$
\mathbf{A}^{N} | a \rangle = \sum_{k=0}^{N-1} \alpha_k \mathbf{A}^k | a \rangle
$$
but nothing guarantees me that ##\mathbf{A}^{N} | a \rangle ## is not orthogonal to the other vectors, or does it?

Any help appreciated.
 
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  • #2
DrClaude said:
But what if ##c_N \neq 0##? I need to show that ##\mathbf{A}^{N+1} | a \rangle## is in ##\mathcal{M}##. I'm tempted to use the fact that the vectors are linearly dependent to state that I can write
$$
\mathbf{A}^{N} | a \rangle = \sum_{k=0}^{N-1} \alpha_k \mathbf{A}^k | a \rangle
$$
but nothing guarantees me that ##\mathbf{A}^{N} | a \rangle ## is not orthogonal to the other vectors, or does it?

You can't really write this. Linear dependent means (by definition) that there are scalars ##\alpha_k## which are not all zero such that

[tex]\alpha_0 | a \rangle + \alpha_1 \mathbf{A}| a \rangle + ... + \alpha_N \mathbf{A}^N| a \rangle=\mathbf{0}[/tex]

Now, you have two situations. Either ##\alpha_N\neq 0##, and in that case, you can write something like you wrote in the quote. In the case that ##\alpha_n=0##, you need to do something differently. In this case, they key fact is that at least one of the ##\alpha_k## is nonzero.

You can handle both cases simultaniously by remarking that there exists an ##M\leq N## and ##\alpha_k## scalars such that

[tex]\mathbf{A}^M | a \rangle = \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^k| a \rangle[/tex]
 
Last edited:
  • #3
This isn't precalculus (although at my university, we studied it before calculus. :smile: ) I'm moving it to calculus & beyond.
 
  • #4
Fredrik said:
This isn't precalculus (although at my university, we studied it before calculus. :smile: ) I'm moving it to calculus & beyond.

Sorry about that. I hesitated for a while as to which forum to post to.

micromass said:
You can't really write this. Linear dependent means (by definition) that there are scalars ##\alpha_k## which are not all zero such that

[tex]\alpha_0 | a \rangle + \alpha_1 \mathbf{A}| a \rangle + ... + \alpha_N \mathbf{A}^N| a \rangle=\mathbf{0}[/tex]

Now, you have two situations. Either ##\alpha_N\neq 0##, and in that case, you can write something like you wrote in the quote. In the case that ##\alpha_n=0##, you need to do something differently. In this case, they key fact is that at least one of the ##\alpha_k## is nonzero.


That sums up my problem quite well. If indeed ##\alpha_N=0##, then ##\mathbf{A}^N | a \rangle## is linearly independent of the other vectors, so what does that say about ##\mathbf{A}^{N+1} | a \rangle##?

micromass said:
You can handle both cases simultaniously by remarking that there exists an ##M\leq N## and ##\alpha_k## scalars such that

[tex]\mathbf{A}^M | a \rangle = \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^k| a \rangle[/tex]
Again, I don't see how to treat the case where ##M < N##.
 
  • #5
DrClaude said:
Again, I don't see how to treat the case where ##M < N##.

OK. Can you tell me how you would dela with the case ##M=N##? You don't have to write it out completely, a summary would suffice.
 
  • #6
Assuming that
$$
\mathbf{A}^N |a \rangle = \sum_{k=0}^{N-1} \alpha_{k} \mathbf{A}^k |a \rangle
$$
we have
$$
\begin{align}
\mathbf{A}^{N+1} |a \rangle &= \mathbf{A} \sum_{k=0}^{N-1} \alpha_{k} \mathbf{A}^k |a \rangle \\
&= \sum_{k=0}^{N-1} \alpha_{k} \mathbf{A}^{k+1} |a \rangle \\
&= \sum_{k=1}^{N} \alpha_{k-1} \mathbf{A}^{k} |a \rangle
\end{align}
$$
which, by definition of ##\mathcal{M}## is in ##\mathcal{M}##, meaning that ##\mathbf{A} \left( \mathbf{A}^{N} |a \rangle \right) \in \mathcal{M}##, completing the "proof" that ##\mathbf{A} | x \rangle \in \mathcal{M}## for ##| x \rangle \in \mathcal{M}##.
 
  • #7
Alright. So the basic idea was to multiply by ##\mathbf{A}##.

This doesn't work in the case ##M<N##. But maybe multiplying with something else works? Like ##\mathbf{A}^2## or ##\mathbf{A}^3##?
 
  • #8
Aside: Since I have joined PF, I've mostly posted answers to questions posed by others. It feels very strange to be on the other side. It's been a long time since I've been a student...

Ok, I think I see the light. From the linear dependence of the vectors spanning ##\mathcal{M}##, we have that for some ##M \leq N##
$$
\mathbf{A}^M | a \rangle = \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^k | a \rangle
$$
with at least one ##\alpha_k \neq 0##. Setting ##p = N-M+1##, we find
$$
\begin{align}
\mathbf{A}^p \mathbf{A}^M | a \rangle &= \mathbf{A}^p \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^k | a \rangle \\
&= \sum_{k=0}^{M-1} \alpha_k \mathbf{A}^{p+k} | a \rangle \\
\mathbf{A}^{N+1} | a \rangle &= \sum_{k=p}^{N} \alpha_{k-p} \mathbf{A}^{k} | a \rangle
\end{align}
$$
from which we conclude that ##\mathbf{A}^{N+1} | a \rangle \in \mathcal{M}##.

Hope I got it right. Thanks micromass for the help.
 
  • #9
That's it! Congratz!
 

1. What is a linear operator?

A linear operator is a mathematical function that maps one vector space to another, while preserving the algebraic properties of vector addition and scalar multiplication. In other words, it is a transformation that preserves the shape and orientation of vectors in a vector space.

2. How does a linear operator act on vectors?

A linear operator acts on vectors by multiplying each vector by a fixed matrix, known as the operator matrix. This multiplication can be represented as a linear combination of the original vector, where each component is multiplied by a scalar value determined by the operator matrix.

3. What is the significance of a linear operator in mathematics?

Linear operators are essential in many areas of mathematics and physics, as they provide a way to describe and manipulate geometric objects in a consistent and rigorous manner. They are particularly useful in linear algebra, functional analysis, and quantum mechanics.

4. How are linear operators represented mathematically?

Linear operators can be represented in various ways, depending on the context. In general, they can be represented as matrices, differential equations, or abstract functions. In linear algebra, they are typically represented as square matrices, while in functional analysis, they are represented as continuous linear transformations.

5. What is the difference between a linear operator and a vector?

A linear operator is a function that operates on vectors, while a vector is an element of a vector space that can be transformed by a linear operator. In other words, a linear operator is a rule, while a vector is an object that follows that rule when acted upon by the operator.

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