Linear Transformations: One-to-One and Onto Conditions

In summary, linear transformations are mathematical functions that map one vector space to another. They can be one-to-one, meaning each input has a unique output, or onto, meaning every element in the output space is mapped from the input space. For a linear transformation to be one-to-one, the null space must only contain the zero vector, while for it to be onto, the range must equal the output space. These conditions are important in understanding the properties and applications of linear transformations in various fields, such as physics, engineering, and computer science.
  • #1
playboy
A question reads:

Let T: V-->W be a linear transformation.

a) If T is one-to-one and TR=TR1 for transformations R and R1: U -->V, show that R = R1
b) If T is onto and ST=S1T for transformations S and S1: W -->U, show that S=S1

I am sooo very lost here, and no idea where to start:(

for part a) what does it mean by TR and TR1?
just T(R): U --->V and T(R1): U ---V?

If its onto, dosn't that just mean T(R) = o and then show that R = 0?

I won't even bother with part be given that I am confused with part A

Can somebody help me please

Thanks
 
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  • #2
Definitions:

If A is a map from X to Y, and B is a map from Y to Z, then BA is a map from X to Z defined by:

BA(x) = B(A(x))

for all x in X. So, if f(x) = 3x, and g(x) = x+5, then gf(x) = g(f(x)) = g(3x) = 3x+5. Note that fg(x) = f(g(x)) = f(x+5) = 3(x+5) = 3x+15, so gf is not the same as fg.

If A is a map from X to Y and B is also a map from X to Y, then A = B if and only if, for all x in X, A(x) = B(x).

A map A from X to Y is one-to-one if and only if, for all x and x' in X, A(x) = A(x') if and only if x = x'. In simpler terms, a one-to-one map never sends two different things to the same element; each element of the co-domain gets its own image. The map f on the reals defined by f(x) = x² is not one-to-one, because it sends both 1 and -1 to the same thing, namely 1.

A map A from X to Y is onto if and only if, for all y in Y, there exists some x in X such that A(x) = y. In simpler terms, a map is onto if it reaches everything in the codomain. The map f in the previous example is not onto, since it will only map into the non-negative half of the reals.
 
  • #3
no progress so far :(
 
  • #4
playboy said:
A question reads:
Let T: V-->W be a linear transformation.
a) If T is one-to-one and TR=TR1 for transformations R and R1: U -->V, show that R = R1
b) If T is onto and ST=S1T for transformations S and S1: W -->U, show that S=S1
I am sooo very lost here, and no idea where to start:(
for part a) what does it mean by TR and TR1?
just T(R): U --->V and T(R1): U ---V?
No, R takes a vector in U to one in V, then T takes it to a vector in W.
TR: U-> W That is, if x is in U, apply R to it to get R(x) in V. Now apply T to that to get T(R(x)) in W. It is essentially composition of functions.
If its onto, dosn't that just mean T(R) = o and then show that R = 0?
No, "one-to-one" means that if T(x)= T(y) then x= y: two different vectors in V cannot[\b] be mapped to the same vector in W. For T a linear transformation, T(0)= 0 so "one-to-one" for a linear transformation is the same as saying T(x)= 0 if and only if x= 0.
I won't even bother with part be given that I am confused with part A
Can somebody help me please
Thanks

TR= TR1 means that for every vector x in U, TR(x)= TR1(x). That is,
T(R(x))= T(R1(x)). Since T is one-to-one, what does that tell you about
R(x) and R1(x)? Since x could be any vector in U, what does that tell you about R and R1?

"onto" means that the range of T is all of W: if y is any vector in W, then there is some x in V so that T(x)= y. (for example, x2 is not "onto" the set of all real numbers since no x2 is equal to -1. It is "onto" the set of all non-negative numbers.)

Again ST= S1T1 means that S(T(x))= S1(T(x)) for all x in V.
Let y be any vector in W. Then there exist x such that y= T(x). For that x, S(T(x))= S(y)= S1(T(x))= S1(y). That is, for every y in W, S(y)= S1(y).
 
  • #5
HallsofIvy said:
TR= TR1 means that for every vector x in U, TR(x)= TR1(x). That is, T(R(x))= T(R1(x)). Since T is one-to-one, what does that tell you about R(x) and R1(x)? Since x could be any vector in U, what does that tell you about R and R1?
Since T is one-to-one, that tells me that R(x) = R1(x)
And since X is any vector in U, then that tells me that R = R1
 
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  • #6
Okay I am back again with a similar question.

Let V---(T)--->U---(S)---W be linear transformations.
a) If ST is one-to-one, show that T is one-to-one and that dimV </= dim U

Here is how i attempted it:

Assume T is NOT one-to-one

if x is a vector in V, then for ever x not equal to 0 such that T(x) = 0

then
ST(x) = 0
=S(T(x))
=S(0)
therefore, T is one-to-one?

How does that sound?

Now how do i show that dimV is </= dim U?
 
  • #7
I'm not sure what you mean by this: "if x is a vector in V, then for ever x not equal to 0 such that T(x) = 0"
It doesn't have to be for "ever(y) x not equal to 0". It is true that if T is not one-to-one, then there must exist some x, not equal to 0, such that T(x)= 0. Then, of course, ST(x)= S(0)= 0 which contradicts the fact that S is one-to-one.

Now, apply T to every basis vector of V. How many vectors does give you? What does that tell you about the dimension of T(V) (which is a subspace of U)?
 
  • #8
Okay I am so lost. Ill start again.

Let x be a basis vector in V, not equal to 0, such that T(x) = 0
Then ST(x) = S(0) = 0 contradicts that S is one-to-one?
How does this show that T is one-to-one

If T is one-to-one, ker(T) = 0. therefore, dimV = dimImage(T) and Image(T) = U... so dimV = dimU

How does that sound?
 
  • #9
oh i don't know how i missed that, i see why it contradicts one-to-one! I am so silly.

Now for the dimension,

If you apply T to ever basis in V, since T is one-to-one, you will get that many vectors. And since V is a subspace of U, its dimension cannot be greater than that of U.

How does that sound? (If anyone actually seems to care :S)
 

Related to Linear Transformations: One-to-One and Onto Conditions

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another vector space while preserving the structure of the original space. It is a type of function that obeys the rules of linearity, meaning that the output of the function is a linear combination of the input variables.

2. How is a linear transformation represented?

A linear transformation can be represented by a matrix. The columns of the matrix represent the images of the basis vectors of the original vector space in the new vector space. The transformation can also be represented by a system of linear equations.

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

A linear transformation preserves the structure of the original vector space, while a nonlinear transformation does not. This means that a linear transformation maintains properties such as linearity and proportionality, while a nonlinear transformation does not. A linear transformation can be represented by a matrix, while a nonlinear transformation cannot.

4. What are some examples of linear transformations?

Some common examples of linear transformations include rotations, reflections, and scaling. These transformations can be represented by matrices and preserve properties such as linearity and proportionality. Other examples include shearing, translation, and projection.

5. How are linear transformations used in real life?

Linear transformations have many applications in real life, particularly in fields such as physics, computer graphics, and engineering. For example, linear transformations are used to describe the motion of celestial bodies, to create computer-generated images, and to model the behavior of electrical circuits. They are also used in data analysis and machine learning to make predictions and classifications.

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