Linearity and Orthogonality of Inner Product in Vector Space H

In summary: For ii) you'll need to use integration by parts and the fact that \lambda(x) is smooth. In summary, The conversation discusses the definition and properties of the inner product in a real-valued vector space. It defines linearity and orthogonality and explores the possibility of defining the inner product using different operations, such as integration and derivatives. The conclusion is that all three given operations can be defined as inner products, with some conditions and proofs required for one of the options.
  • #1
squenshl
479
4

Homework Statement


Denote the inner product of f,g [itex]\in[/itex] H by <f,g> [itex]\in[/itex] R where H is some(real-valued) vector space
a) Explain linearity of the inner product with respect to f,g. Define orthogonality.
b) Let f(x) and g(x) be 2 real-valued vector functions on [0,1]. Could the inner product be defined as (give reasons)
i) <f,g> = integral from 0 to 1 of f(x)g(x) dx?
ii) <f,g> = integral from 0 to 1 [itex]\lambda[/itex](x)f'(x)g'(x) dx? where prime denotes the derivative and [itex]\lambda[/itex](x) > 0 is a smooth function (assuming f',g' [itex]\in[/itex] H)?
iii) <f,g> = f(x)g(x)?

Homework Equations





The Attempt at a Solution


a) That is just trivial
b) Not too sure on any of them
 
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  • #2
For i) since the integral is a real number (provided that f and g are continuous, differentiable etc.) once computed suggest that this will be an inner product, similarly for ii) and iii) (except the integral part for iii))
 
  • #3
An inner product must be such that
[tex]<au+ bv, w>= a<u, w>+ b<v, w>[/tex]
[tex]<u, v>= \overline{<v, u>}[/tex]

Can you show that those are true for each of the given operations?
 
  • #4
Do I show positive definiteness <f,f> >= 0? and <f,f> = 0 if and only if f = 0
 
  • #5
I just proved all 3 axioms to be an inner product.
I got that all 3 operations are all defined as inner products, is this correct (I'm a little skeptical on ii))?
i) and iii) are easy.
 

Related to Linearity and Orthogonality of Inner Product in Vector Space H

1. What is the definition of linearity of inner product in vector space H?

The linearity of inner product in vector space H refers to the property of an inner product that satisfies the rules of additivity and homogeneity. This means that the inner product of two vectors will be equal to the sum of their individual inner products and that the inner product of a vector with a scalar will be equal to the scalar multiplied by the inner product of the vector.

2. How is the inner product of two vectors calculated in vector space H?

The inner product of two vectors in vector space H is calculated by multiplying the corresponding components of the vectors and then summing them up. This is also known as the dot product.

3. What is the significance of linearity of inner product in vector space H?

The linearity of inner product in vector space H allows us to perform mathematical operations on vectors using inner products, which can simplify calculations and make them more efficient. It also allows us to extend the concept of orthogonality to vector spaces.

4. What is orthogonality in vector space H?

Orthogonality in vector space H refers to the property of two vectors being perpendicular to each other. This means that their inner product will be equal to 0.

5. How is the orthogonality of two vectors determined in vector space H?

The orthogonality of two vectors in vector space H can be determined by calculating their inner product. If the inner product is equal to 0, then the vectors are orthogonal. Additionally, two vectors are orthogonal if and only if their dot product is equal to 0.

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