Inner Product of a Linear Transformation

In summary, the conversation discusses the definition of an inner product on a vector space and how it relates to the linearity of a function T from that vector space to an inner product space. The participants in the conversation use a series of equations and logical arguments to prove that <x,y>' = <T(x),T(y)> defines an inner product on V if and only if T is one-to-one.
  • #1
gysush
26
0

Homework Statement


Let V be a vector space over a field F = R or C. Let W be an inner product space over F. w/ inner product <*,*>. If T: V->W is linear, prove <x,y>' = <T(x),T(y)> defines an inner product on V if and only if T is one-to-one

Homework Equations


What we know, W is an inner product space, so it satisfies for x,y,z in W and c in F the properties of inner product space:
<x + z, y> = <x,y> + <z,y>
<cx,y>=c<x,y>
<x,y>= conjugate<y,x>
<x,x> > 0 if x does not = 0

T linear, thus for x,y in V and c in F
T(x+cy)=T(x) + cT(y)

Forward phase:
If <x,y>' = <T(x),T(y)> is an inner product then it satisfies the same requirements for an inner product space mentioned above.

Want to show T is one-to-one. => by def. if T: V-> W is one-to-one then for x,y in V T(x),T(y) in W...T(x)=T(y) => x=y...or equivalently the contra-positive.
Also, T is one-to-one iff Ker(T) = {0}

By def. an inner product on V is a function that assigns x,y in V/F to a scalar in F denoted by <x,y>

3. Attempt

We want to show that if T(x)=T(y) then x=y...
consider <T(x),T(y)> = <T(x),T(x)> which is > 0 unless T(x) = 0.
Now, I am failing to see how this helps at all. If <T,T> is > 0...than how does this give us any information about x and y? If T(x)=0...then this argument is the Ker argument...

So...Consider all x in V s.t. T(x)=0...we want to show x=0.

then...<T(x),T(y)> = < 0 , T(y)> = 0 = <T(y),0>

Again..I do not see how this gives us any info about x and y.

Any starting hints?
 
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  • #2
Try forming T(x-y), and then form <T(x-y),T(x-y)>. I think this will give you the answer.
 
  • #3
If [itex]\left\langle Tx,Ty\right\rangle[/itex] is an inner product, what can you say about [itex]\left\langle Tx,Tx\right\rangle[/itex]? What does this tell you about [itex]ker\left( T\right)[/itex]?
 
  • #4
I think I see what I was missing. I was trying to only manipulate <T(x),T(y)>.
Here is what I think now...

Let T(x)=T(y) and consider <x-y,x-y>' = <T(x-y),T(x-y)> = <T(x),T(x-y)> - <T(y),T(x-y)> Then, <x-y,x-y>' = <T(x),T(x-y)> - <T(x),T(x-y)> = 0
Then, <a,a>' = 0 implies a=0 => x-y = 0 => x=y

Then for the reverse direction Let T(x)=T(y) => x=y...consider <x + z, y>' = <T(x+z),T(y)>
then <T(x+z),T(y)> = <T(x),T(y)> + <T(z),T(y)> since W is inner product space
which equals <x,y>' + <z,y>'

Then similarly for the other the other conditions.
 

1. What is the definition of the inner product of a linear transformation?

The inner product of a linear transformation is a mathematical operation that takes two vectors as inputs and produces a scalar value as an output. It is a way to measure the similarity or difference between two vectors in a vector space.

2. How is the inner product of a linear transformation calculated?

The inner product of a linear transformation is calculated by multiplying the transpose of the first vector by the second vector. This can also be represented as the dot product of the two vectors.

3. What are the properties of the inner product of a linear transformation?

Some of the main properties of the inner product of a linear transformation include linearity, symmetry, and positive definiteness. Linearity means that the inner product is distributive and multiplicative over addition. Symmetry means that the order of the vectors does not affect the result of the inner product. Positive definiteness means that the inner product of a vector with itself is always greater than or equal to zero, and only equal to zero when the vector is the zero vector.

4. What is the significance of the inner product of a linear transformation in mathematics and science?

The inner product of a linear transformation has many applications in mathematics and science. It is used to define important concepts such as orthogonality, distance, and angle between vectors. It also plays a crucial role in areas such as signal processing, quantum mechanics, and machine learning.

5. How does the inner product of a linear transformation relate to the concept of a matrix?

The inner product of a linear transformation can be represented using matrices. Specifically, the transpose of the first vector can be represented as a row matrix, and the second vector can be represented as a column matrix. The inner product can then be calculated by multiplying these two matrices. Additionally, the properties of matrices, such as the distributive and multiplicative properties, also apply to the inner product of a linear transformation.

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