Linear Maps in $\mathbb{R}^n$ and $\mathbb{R}^m$: Proving Properties with $M$

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  • Thread starter mathmari
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In summary: Confident)If we treat $M$ as a matrix that represents a linear transformation, and if we take $M(\phi)$ to mean the composition $M\circ\phi$, we get indeed:$$(M(\phi +\psi ))(x)=(M\circ(\phi +\psi ))(x) = M((\phi +\psi )(x))$$
  • #1
mathmari
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Hey! :eek:

Let $1\leq n,m\in \mathbb{N}$ and let $\phi, \psi:\mathbb{R}^n\rightarrow \mathbb{R}^m$ be linear maps. Let $\lambda\in \mathbb{R}$.

Show the following:

  1. $M(\phi +\psi )=M(\phi )+M(\psi )$
  2. $M(\lambda \phi )=\lambda M(\phi )$

What exactly is $M$, it is not defined in this exercise? Is it a matrix? (Wondering)
 
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  • #2
Hey mathmari!

I don't know what M is either. It doesn't look like a matrix. (Worried)

I think it must have been defined somewhere in your text.
 
  • #3
Klaas van Aarsen said:
I don't know what M is either. It doesn't look like a matrix. (Worried)

I think it must have been defined somewhere in your text.

Can we show the properties if we suppose that $M$ is a map? (Wondering)
 
  • #4
mathmari said:
Can we show the properties if we suppose that $M$ is a map? (Wondering)

$M$ must be a function that maps functions to some vector space so that addition and multiplication with a real scalar are defined.

Suppose we assume $M$ is an arbitrary map.
Can we find a counter example so that $M$ is not linear? (Wondering)

Suppose we pick $\phi: x\mapsto 0$ and $\psi: x\mapsto 0$.
And suppose we pick $M: (\mathbb R^n\to\mathbb R^m)\to \mathbb R$ given by $\chi \mapsto 1$.
Are the linearity conditions satisfied? (Wondering)
 
  • #5
Klaas van Aarsen said:
$M$ must be a function that maps functions to some vector space so that addition and multiplication with a real scalar are defined.

Suppose we assume $M$ is an arbitrary map.
Can we find a counter example so that $M$ is not linear? (Wondering)

Suppose we pick $\phi: x\mapsto 0$ and $\psi: x\mapsto 0$.
And suppose we pick $M: (\mathbb R^n\to\mathbb R^m)\to \mathbb R$ given by $\chi \mapsto 1$.
Are the linearity conditions satisfied? (Wondering)
I got stuck right now, how is the map defined? (Wondering) I saw now that the title of this exercise is "Linear Maps and Matrices". So is maybe $M$ related to matrices? (Wondering)
 
  • #6
mathmari said:
I got stuck right now, how is the map defined?

Let's define $L$ as the set of linear functions in $\mathbb R^n\to\mathbb R^m$, so that $\phi, \psi\in L$.
An example of an element $\phi\in L$ is the function given by $\phi(\mathbf x)=\mathbf 0$ that maps any $\mathbf x\in\mathbb R^n$ to the zero element $\mathbf 0\in \mathbb R^m$ yes? (Thinking)

Now $M$ must be a function that has an element like $\phi\in L$ as input.
And its output must be something that we can add, and that we can multiply with a real scalar.
Let's define the co-domain of $M$ as $V$ for which addition and scalar multiplication are defined.
Then $M: L\to V$, isn't it? (Wondering)

As an example, let's pick again $\phi\in L$ given by $\phi(\mathbf x)=\mathbf 0$.
Then $M(\phi)$ must be an element of $V$ yes? (Thinking)
Let $\mathbf v$ be a non-zero element in $V$.
Continuing the example, we can pick $M(\phi)=\mathbf v$, can't we? (Wondering)
Moreover, we can choose $M$ such that for any $\chi\in L$ we have $M(\chi)=\mathbf v\ne\mathbf 0$.

If we pick this $M$, and suppose we have some $\phi,\psi\in L$, what will $M(\phi)$, $M(\psi)$, and $M(\phi+\psi)$ be? (Wondering)

mathmari said:
I saw now that the title of this exercise is "Linear Maps and Matrices". So is maybe $M$ related to matrices?

Since $M$ is applied to a function and not to a vector, it seems to me that it cannot be a matrix.
It is a 'Map' though, and the question asks to prove that it is a Linear Map. (Thinking)
 
  • #7
Klaas van Aarsen said:
Since $M$ is applied to a function and not to a vector, it seems to me that it cannot be a matrix.
It is a 'Map' though, and the question asks to prove that it is a Linear Map. (Thinking)
I think $M$ is a transformation matrix with respect to the canonical basis. What do we have in this case then? (Wondering)

$M$ is then a linear map, or not? (Wondering)

Then we get the following:

  1. For $x\in \mathbb{R}^n$ we have the following: $$(M(\phi +\psi ))(x)=M(\phi +\psi )(x) \ \overset{\phi, \psi \text{ linear }}{ = } \ M(\phi(x) +\psi(x) ) \ \overset{M \text{ linear }}{ = } \ M(\phi(x)) +M(\psi(x) )=(M(\phi ))(x)+(M(\psi ))(x)$$
  2. For $x\in \mathbb{R}^n$ we have the following: $$(M(\lambda \phi ))(x)=M(\lambda \phi )(x) \ \overset{\phi\text{ linear }}{ = } \ M(\lambda \phi(x) ) \ \overset{M \text{ linear }}{ = } \ \lambda M( \phi(x) )=\lambda (M(\phi ))(x)$$

Is everything correct? (Wondering)
 
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  • #8
mathmari said:
I think $M$ is a transformation matrix with respect to the canonical basis. What do we have in this case then? (Wondering)

$M$ is then a linear map, or not? (Wondering)

Then we get the following:

1. For $x\in \mathbb{R}^n$ we have the following: $$(M(\phi +\psi ))(x)=M(\phi +\psi )(x) \ \overset{\phi, \psi \text{ linear }}{ = } \ M(\phi(x) +\psi(x) ) \ \overset{M \text{ linear }}{ = } \ M(\phi(x)) +M(\psi(x) )=(M(\phi ))(x)+(M(\psi ))(x)$$

If we treat $M$ as a matrix that represents a linear transformation, and if we take $M(\phi)$ to mean the composition $M\circ\phi$, we get indeed:
$$(M(\phi +\psi ))(x)=(M\circ(\phi +\psi ))(x) = M((\phi +\psi )(x))$$

It does not follow from the linearity condition that:
$$M((\phi +\psi )(x)) \ \overset{\phi, \psi \text{ linear }}{ = } \ M(\phi(x) +\psi(x) )$$
though. (Worried)

Instead it follows from how function addition is usually defined.
That is, functions are added pointwise.
The function $(\phi+\psi)$ is such that for all $x$ we have that $(\phi+\psi)(x)=\phi(x)+\psi(x)$.
So it should be:
$$M((\phi +\psi )(x)) \ \overset{\text{definition of function addition}}{ = } \ M(\phi(x) +\psi(x) )$$

Then we can indeed use that Matrix multiplication is linear to find:
$$M(\phi(x) +\psi(x) ) \ \overset{M \text{ linear }}{ = } \ M(\phi(x)) +M(\psi(x) )
=(M\circ\phi)(x)+(M\circ\psi)(x)=(M(\phi ))(x)+(M(\psi ))(x)$$
(Nod)
mathmari said:
2. For $x\in \mathbb{R}^n$ we have the following: $$(M(\lambda \phi ))(x)=M(\lambda \phi )(x) \ \overset{\phi\text{ linear }}{ = } \ M(\lambda \phi(x) ) \ \overset{M \text{ linear }}{ = } \ \lambda M( \phi(x) )=\lambda (M(\phi ))(x)$$

Is everything correct?

Same here:
$$(\lambda\phi)(x) \ \overset{\text{ definition of scalar multiplication with a function}}{ = } \ \lambda(\phi(x))$$
(Thinking)
 
  • #9
We have some new information.
In your latest thread I noticed:
mathmari said:
Let $ \sigma_a: \mathbb{R}^2 \rightarrow \mathbb{R}^2 $ be the reflection on the straight line through the origin, where $ a $ describes the angle between the straight line and the positive $ x $ axis.

...

Determine the matrix $s_a: = M (\sigma_a)$.

It implies that $M(\phi)$ is the matrix that represents the linear map $\phi: \mathbb R^n\to\mathbb R^m$. (Thinking)
 
  • #10
Klaas van Aarsen said:
We have some new information.
In your latest thread I noticed:It implies that $M(\phi)$ is the matrix that represents the linear map $\phi: \mathbb R^n\to\mathbb R^m$. (Thinking)

Ah ok.. So can we don't that as in post #8 ? (Wondering)
 
  • #11
mathmari said:
Ah ok.. So can we don't that as in post #8 ?

Not quite, because $M(\phi(x))$ is not well defined.
That is, $\phi(x)$ is a vector, and we can't get a matrix from a vector - at least not as intended. (Thinking)

I think it must be as follows.

The matrix of a linear map $\chi:\mathbb R^n\to\mathbb R^m$ is:
$$M(\chi) = \begin{pmatrix}\chi(\mathbf e_1)\cdots \chi(\mathbf e_n)\end{pmatrix}$$
So:
\begin{align*}M(\phi+\psi)
&=\Big[(\phi+\psi)(\mathbf e_1)\,\cdots\, (\phi+\psi)(\mathbf e_n)\Big]\\
&=\Big[(\phi(\mathbf e_1)+\psi(\mathbf e_1))\,\cdots\, (\phi(\mathbf e_n)+\psi(\mathbf e_n))\Big]\\
&=\Big[\phi(\mathbf e_1)\,\cdots\, \phi(\mathbf e_n)\Big]
+\Big[\chi(\mathbf e_1)\,\cdots\, \chi(\mathbf e_n)\Big]\\
&=M(\phi)+M(\psi)\\
\end{align*}
(Thinking)
 

FAQ: Linear Maps in $\mathbb{R}^n$ and $\mathbb{R}^m$: Proving Properties with $M$

What is a linear map in $\mathbb{R}^n$ and $\mathbb{R}^m$?

A linear map, also known as a linear transformation, is a function that preserves the algebraic structure of vector spaces. In simpler terms, it maps vectors from one vector space to another in a way that respects addition and scalar multiplication. In $\mathbb{R}^n$ and $\mathbb{R}^m$, the linear map takes in vectors with n and m components respectively, and outputs vectors with m and n components respectively.

How do you prove properties of linear maps using $M$?

To prove properties of linear maps using $M$, you first need to understand what $M$ represents. $M$ is the matrix representation of the linear map, where each column of $M$ represents the image of the corresponding standard basis vector. To prove a property, you can manipulate $M$ using matrix operations and properties, and then translate the result back into the language of linear maps.

What are some common properties of linear maps in $\mathbb{R}^n$ and $\mathbb{R}^m$?

Some common properties of linear maps in $\mathbb{R}^n$ and $\mathbb{R}^m$ include linearity (preserving addition and scalar multiplication), injectivity (one-to-one mapping), surjectivity (onto mapping), and bijectivity (both injective and surjective). Other properties may include matrix representations, nullity, and rank.

How can you determine if a linear map is invertible?

A linear map is invertible if and only if it is bijective. In other words, the map must be both one-to-one and onto. To determine if a linear map is invertible, you can check if its matrix representation has a nonzero determinant. If the determinant is nonzero, the map is invertible. If the determinant is zero, the map is not invertible.

Can linear maps be composed or multiplied?

Yes, linear maps can be composed or multiplied, just like functions. The composition of two linear maps is also a linear map, and the multiplication of two linear maps results in a new linear map. The order in which the maps are composed or multiplied matters, as the resulting map may be different depending on the order.

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