Why <v,v> >= 0? Understanding Inner Product Definition

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In summary: DIn summary, the conversation discusses the definition of an inner product and how it applies to vectors with complex numbers. The definition for an inner product is expressed as Ʃ(v_{j})(\overline{v_{j}}) for 1≤j≤n where n is the length of vector v. It is noted that for vectors in ℝ, the inner product will never equal zero unless the vector itself is zero. The conversation also addresses the issue of conjugating the first term in the vector, rather than the first term in each product, which affects the resulting vector product.
  • #1
9k9
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I think I am missing a subtle point of the definition of a inner product. All the texts I have seen state <v,v> >= 0

If you have say:

v=(1,2i)

then <v,v> = -3 (Using the definition where you do the dot product, while conjugating the first term)

This is a negative number and defies the above definition of an inner product.

Is it that (1,2i) is not in an inner product space and therefore doen't have an inner product?
 
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  • #2
welcome to pf!

hi 9k9! welcome to pf! :smile:

(1,2i).(1,-2i) = 1.1 + 2i.-2i = 1 - 4i2 = 5 :wink:
 
  • #3
Welcome to PF, 9k9! :smile:

The inner product for vectors with complex numbers is defined with a complex conjugate (as tt showed).

Without it, you have shown yourself that the resulting vector product does not satisfy the axioms for an inner product.
 
  • #4
It seems to me that you did not conjugate the first term.
Perhaps you can write out your calculation?
 
  • #5
Thanks for the welcomes, I see now I didn't read the definiton correctly and I was only trying to conjugate the first element in the vector, rather than the first term in each of the products.
 
  • #6
9k9 said:
Using the definition where you do the dot product, while conjugating the first term

What do you mean by "the first term"? State the definition you are using.
 
  • #7
<v,v>=0 only when v=0

the definition for inner-product is Ʃ(v[itex]_{j}[/itex])([itex]\overline{v_{j}}[/itex]) for 1≤j≤n where n is the length of vector v
note that [itex]\overline{v_{j}}[/itex] is defined as the adjoint, or conjugate transpose

when dealing in ℝ, you'll never get <v,v>=0 because it is merely taking the square of each term {(a[itex]_{1}[/itex])[itex]^{2}[/itex]+...+(a[itex]_{k}[/itex])[itex]^{2}[/itex]}
for all k[itex]\epsilon[/itex]dim(v) and then taking the square root of that sum
√(Ʃa[itex]_k{}[/itex]) which means that the inside sum must be ≥0 else the inner product wouldn't exist because we are in ℝ,
but would also never equal zero unless v=0 and then 0[itex]^{2}[/itex]=0

it's the same for ℂ since squaring terms ends up those new terms becoming positive, rather non-zero and non-negative
 
  • #8
Welcome to PF!

Hi Krovski! Welcome to PF! :smile:
Krovski said:
<v,v>=0 only when v=0

Ah, no … that fooled me at first, too …

it's "<v,v> >= 0" …

and the >= is because 9k9 doesn't have a Mac with a key that types "≥" ! :biggrin:
 
  • #9


tiny-tim said:
Hi Krovski! Welcome to PF! :smile:


Ah, no … that fooled me at first, too …

it's "<v,v> >= 0" …

and the >= is because 9k9 doesn't have a Mac with a key that types "≥" ! :biggrin:

good catch and thank you
 

1. Why is it important to understand the definition of inner product?

Understanding the definition of inner product is crucial for understanding various mathematical concepts, such as vector spaces, orthogonality, and projections. It also has applications in fields such as physics, engineering, and computer science.

2. What is the significance of the condition >= 0 in the definition of inner product?

The condition >= 0 ensures that the inner product always produces a non-negative result, which is important in applications where negative values do not make sense. It also allows for the use of geometric interpretations and properties, such as the Cauchy-Schwarz inequality.

3. Can ever be equal to 0 in an inner product space?

Yes, in certain cases, can equal 0. For example, if v is the zero vector, then = 0. However, in most cases, the inner product is defined such that = 0 only if v is the zero vector.

4. How is the inner product related to the norm of a vector?

The norm of a vector is defined as the square root of the inner product of the vector with itself, i.e. ||v|| = √. In other words, the inner product provides a way to calculate the length or magnitude of a vector, which is useful in many applications.

5. What are some common properties of inner products?

Some common properties of inner products include linearity in the first argument, conjugate symmetry, and positive-definiteness. These properties allow for the manipulation and simplification of inner products in various mathematical calculations.

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