Some linear transformation

In summary, a linear transformation is a mathematical function that maps a vector from one vector space to another while preserving the basic properties of the vector space. It can be represented by a matrix and is different from a nonlinear transformation in that it preserves the basic properties of the vector space. To determine if a transformation is linear, you can check if it satisfies the properties of linearity or represent it as a matrix and check for matrix multiplication rules. Linear transformations have various real-life applications such as image and signal processing, data compression, computer graphics, and modeling and analyzing systems in physics and engineering.
  • #1
Bertrandkis
25
0
Question 1
Let T: P2 -> M22 be a linear transformation such that

[tex]
T(1+t)=\left[\begin{array}{cc}1&0\\0&0\end{array}
\right];[/tex]
[tex]
T(t+t^{2})=\left[\begin{array}{cc}0&1\\1&0\end{array}
\right];[/tex]
[tex]
T(1+t^{2})=\left[\begin{array}{cc}0&1\\0&1\end{array}
\right];[/tex]
Then find[tex] T(1),T(t),T(t^{2})[/tex]

My attempt
All I know is that [tex] 1,t,t^{2}[/tex] are basis of P2, what do I do next?
How do I find them from given matrices?


Question 2

let dim(v)=n and dim(W)=m and P:V->W be a linear transformation, i.e P(v)=0 for all v in V. Show that the matrix of P with respect to any bases for V and W is the mxn zero matrix.
My attempt
Let S be a basis of V S={v1,v2,...vn}
Let v a vector in v
[tex]v=c1v1+c2v2+ ...cnvn[/tex]

[tex]P(v)=c1w1+c2w2+ ...+cnwm=0[/tex]
Because vectors of S are linearly independant c1,c2 ... cn are all 0
So the resultant matrix of P is a zero matrix



Question 3

Let L:V->W be a linear transformation. show that L is one to one if and only if dim(range L)=dim(V)
My attempt:
We know that dim(V)=dim(range L)+dim(ker L) (1)
if dim(V)>dim(range L) then dim(ker L) is not 0 and L is not One to one
if dim(V)=dim(range L) then dim(V)-dim(range L) = dim(ker L)
and dim(ker L)=0 hence L is one to one.
 
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  • #2
Lets take one at a time. For the first one, notice that T is a linear transformation. What does that imply?
 
Last edited:
  • #3
Bertrandkis said:
Question 1
Let T: P2 -> M22 be a linear transformation such that

[tex]
T(1+t)=\left[\begin{array}{cc}1&0\\0&0\end{array}
\right];[/tex]
[tex]
T(t+t^{2})=\left[\begin{array}{cc}0&1\\1&0\end{array}
\right];[/tex]
[tex]
T(1+t^{2})=\left[\begin{array}{cc}0&1\\0&1\end{array}
\right];[/tex]
Then find[tex] T(1),T(t),T(t^{2})[/tex]
You just stated [itex]T(1), T(2), T(t^2)[/tex]!? Don't you mean "find T(p) where p is any member of P2"? As Siddharth said, T is linear. Any member of P2 can be written at2+ bt+ c. What is T(at2+ at+ b)?


[/quote]My attempt
All I know is that [tex] 1,t,t^{2}[/tex] are basis of P2, what do I do next?
How do I find them from given matrices?


Question 2

let dim(v)=n and dim(W)=m and P:V->W be a linear transformation, i.e P(v)=0 for all v in V. Show that the matrix of P with respect to any bases for V and W is the mxn zero matrix.
My attempt
Let S be a basis of V S={v1,v2,...vn}
Let v a vector in v
[tex]v=c1v1+c2v2+ ...cnvn[/tex]

[tex]P(v)=c1w1+c2w2+ ...+cnwm=0[/tex]
Because vectors of S are linearly independant c1,c2 ... cn are all 0
So the resultant matrix of P is a zero matrix[/quote]
Your final equaiton, P(v)= c1w1+ c2w2+ ...+ cnwn= 0, is in W- it says NOTHING about "the vectors of S". If it were true that "c1, c2, ..., cn are all 0", then v would be the 0 vector- and that is not, in general true. Remember that you can write a linear transformation, L:V->W, in given bases for V and W by applying L to each basis vector in V in turn, then writing the result in the basis in W. The coefficients then form a column for the matrix. If {v2, v2, ..., vn} is a basis for V, what is P(v1)? What is P(v2)?



Question 3

Let L:V->W be a linear transformation. show that L is one to one if and only if dim(range L)=dim(V)
My attempt:
We know that dim(V)=dim(range L)+dim(ker L) (1)
if dim(V)>dim(range L) then dim(ker L) is not 0 and L is not One to one
if dim(V)=dim(range L) then dim(V)-dim(range L) = dim(ker L)
and dim(ker L)=0 hence L is one to one.
 
Last edited by a moderator:
  • #4
Question 1 is formulated correctly. They want [tex]T(1);T(t);T(t^{2})[/tex].
Some one has suggested that :
[tex]T(1)=1/2( T(1+t) - T(t+t^{2}) + T(1+t^{2}) ))[/tex]
becaused T being a linear transformation when the RHS expression is developed it yields [tex]T(1)[/tex]. The problem is solved by replacing T(...) in the RHS expression by their given matrices.
In the same way we can find [tex] T(t) [/tex] and [tex] T(t^{2})[/tex]
 
  • #5
Bertrandkis said:
Question 1 is formulated correctly. They want [tex]T(1);T(t);T(t^{2})[/tex].
My mistake. I misread. You are NOT given T(1), T(t), and T(t2) as I thought. You are given T(1+ t), T(1+ t2) and T(t+ t2).

Some one has suggested that :
[tex]T(1)=1/2( T(1+t) - T(t+t^{2}) + T(1+t^{2}) ))[/tex]
becaused T being a linear transformation when the RHS expression is developed it yields [tex]T(1)[/tex]. The problem is solved by replacing T(...) in the RHS expression by their given matrices.
In the same way we can find [tex] T(t) [/tex] and [tex] T(t^{2})[/tex]
Yes, that would work, although I would be inclined to wonder HOW you noticed that
[tex]T(1)=1/2( T(1+t) - T(t+t^{2}) + T(1+t^{2}) ))[/tex]!

Siddharth's original suggestion was to use linearity to say that
[tex]1: T(1+t)= T(1)+ T(t)= \left[\begin{array}{cc}1&0\\0&0\end{array}\right];[/tex]
[tex]2: T(t+t^{2})= T(t)+ T(t^2)= \left[\begin{array}{cc}0&1\\1&0\end{array}\right];[/tex]
[tex]3:T(1+t^{2})=T(1)+ T(t^2)= \left[\begin{array}{cc}0&1\\0&1\end{array}\right];[/tex]
Now treat those as three equations in the three unknown matrices, T(1), T(t), T(t2). For example, adding (1) and (3) gives the equation 2T(1)+ T(t)+ T(t2)= a matrix. Subtracting (2) from that gives 2T(1)= a matrix, giving the equation you have. You can similarly solve for T(t) and T(t2).
 

1. What is a linear transformation?

A linear transformation is a mathematical function that maps a vector from one vector space to another, while preserving the basic properties of the vector space such as addition and scalar multiplication.

2. How is a linear transformation represented?

A linear transformation can be represented by a matrix, where each column of the matrix represents the transformation of a basis vector in the original vector space.

3. What is the difference between a linear transformation and a nonlinear transformation?

A linear transformation preserves the basic properties of the vector space, while a nonlinear transformation does not. This means that a nonlinear transformation may distort angles and lengths of vectors, while a linear transformation will not.

4. How can I determine if a transformation is linear?

To determine if a transformation is linear, you can check if it satisfies the properties of linearity, which include preserving addition and scalar multiplication. You can also represent the transformation as a matrix and check if it satisfies the rules of matrix multiplication.

5. What are some real-life applications of linear transformations?

Linear transformations have many real-life applications, including image and signal processing, data compression, and computer graphics. They are also used in physics and engineering to model and analyze systems with linear dynamics.

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