Inner Product as a Transformation

In summary: If your inner product is over the real numbers then both are fine. If it's over the complex numbers then you'll have to say exactly how your inner product is defined.By definition the inner product is bilinear,that is,linear in eahh variable while the other variable is held constant.By definition the inner product is bilinear,that is,linear in eahh variable while the other variable is held constant. However, in complex spaces this is not entirely correct. If we use the *usual* definition of inner product <u,v> \equiv \sum_{i=1}^n \bar{u}_i v_i, where the bar denotes the complex conjug
  • #1
nautolian
34
0

Homework Statement



Let V be an inner product space. For v ∈ V fixed, show
that T(u) =< v, u > is a linear operator on V .

Homework Equations





The Attempt at a Solution



First to show it is a linear operator, you show that T(u+g)=T(u)+T(g) and T(ku)=kT(u)
So,
T(u+g)=<v, u+g>=<v,u>+<v,g>=T(u)+T(g)
Then T(ku)=<v, ku>=k<v,u>
And since both the results are in the inner product space it is a linear operator on V? However, I don't know if this is right, because can't you not split up the second value like I did? Only the first? A little clarification would be appreciated! Thanks!
 
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  • #2
nautolian said:

Homework Statement



Let V be an inner product space. For v ∈ V fixed, show
that T(u) =< v, u > is a linear operator on V .

Homework Equations





The Attempt at a Solution



First to show it is a linear operator, you show that T(u+g)=T(u)+T(g) and T(ku)=kT(u)
So,
T(u+g)=<v, u+g>=<v,u>+<v,g>=T(u)+T(g)
Then T(ku)=<v, ku>=k<v,u>
And since both the results are in the inner product space it is a linear operator on V? However, I don't know if this is right, because can't you not split up the second value like I did? Only the first? A little clarification would be appreciated! Thanks!

If your inner product is over the real numbers then both are fine. If it's over the complex numbers then you'll have to say exactly how your inner product is defined.
 
  • #3
By definition the inner product is bilinear,that is,linear in eahh variable while the other variable is held constant.
 
  • #4
hedipaldi said:
By definition the inner product is bilinear,that is,linear in eahh variable while the other variable is held constant.

In complex spaces this is not entirely correct. If we use the *usual* definition of inner product [tex] <u,v> \equiv \sum_{i=1}^n \bar{u}_i v_i, [/tex] where the bar denotes the complex conjugate, then
[tex] <u,cv> = c<u,v>, \text{ but } <cu,v> = \bar{c}<u,v>.[/tex]
Note: this definition of inner product gives <u,u> ≥ 0 and real; the other type
[tex] (u,v) \equiv \sum_{i=1}^n u_i v_i [/tex] gives (u,u) = complex number, in general.

RGV
 

Related to Inner Product as a Transformation

What is an inner product as a transformation?

An inner product as a transformation is a mathematical operation that takes two vectors as inputs and produces a scalar value as output. It is often used to measure the angle between two vectors or the length of a vector. It is also known as the dot product or scalar product.

How is an inner product as a transformation calculated?

The inner product of two vectors, u and v, is calculated by multiplying their corresponding components and then summing the results. For example, if u = [u1, u2, u3] and v = [v1, v2, v3], the inner product can be written as u · v = u1v1 + u2v2 + u3v3.

What is the geometric interpretation of an inner product as a transformation?

The inner product can be interpreted geometrically as the product of the lengths of the two vectors and the cosine of the angle between them. This means that a larger inner product indicates a greater similarity or alignment between the two vectors.

What are some applications of inner product as a transformation?

The inner product as a transformation is used in various fields, including physics, engineering, and computer science. It is commonly used in vector calculus, signal processing, and computer graphics. It is also used in machine learning algorithms, such as principal component analysis and support vector machines.

What are the properties of an inner product as a transformation?

An inner product as a transformation has several properties, including commutativity, distributivity, and linearity. It also satisfies the Cauchy-Schwarz inequality, which states that the absolute value of the inner product of two vectors is less than or equal to the product of their lengths. Additionally, the inner product of a vector with itself is always non-negative, and it is equal to zero only when the vector is a zero vector.

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