Parallelogram Equality+Inner Product Spaces

In summary, if a norm satisfies the parallelogram equality, then it can be proven that it must come from an inner product. This is done by using the parallelogram law and polarization identity, and then showing that the space with this inner product satisfies the properties of an inner product space. On the other hand, spaces like l^\infty, l^1, C[a,b], and c_0 do not satisfy the parallelogram law and therefore cannot have an inner product that gives their norms.
  • #1
gravenewworld
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It is true that if a norm satisfies the parallelogram equality then it must come from an inner product right (i.e. < , > is an inner product).? How in the world could you go about proving/showing this?
 
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  • #2
ya that's true; here's how it's done:
a) you need to know the parallelogram law (duh) but also the polarization identity: [tex] \| x+y\|^2 - \|x-y\|^2 = 4<x,y>[/tex]

b) let V be a normed linear space in which the parallelgram law holds. Define <x,y> by the polarisation identity & prove that V with that inner product is an inner product space, and that [tex]\|x\| = \sqrt{<x,x>}[/tex]

b*) see that spaces like [tex]l^\infty[/tex], [tex]l^1[/tex], C[a,b] with the uniform norm, [tex]c_0[/tex], don't satisfy the parallelogram law, and that there's no inner product (by a) ) that gives the norms for those spaces
 
  • #3
You are of course not allowing fields of characteristic 2.
 

What is a parallelogram equality in inner product spaces?

A parallelogram equality in inner product spaces refers to a property of vector spaces with an inner product. It states that the sum of the squares of the lengths of two sides of a parallelogram is equal to the sum of the squares of the lengths of the other two sides. In other words, it is a way to measure the distance between two vectors in an inner product space.

How is a parallelogram equality used in inner product spaces?

The parallelogram equality is used in inner product spaces to define the notion of orthogonality. If the sum of the squares of the lengths of two sides of a parallelogram is equal to the sum of the squares of the lengths of the other two sides, then the two sides are considered orthogonal to each other. This allows us to define a geometric interpretation of inner products and use them to solve problems in geometry and physics.

What is the relationship between parallelogram equality and inner product spaces?

The relationship between parallelogram equality and inner product spaces is that the former is a fundamental property of the latter. In fact, inner product spaces are defined by the existence of an inner product that satisfies the parallelogram equality. This property is crucial in understanding the geometric interpretation of inner products and their applications in various fields of science and engineering.

Can parallelogram equality be extended to higher dimensions?

Yes, the parallelogram equality can be extended to higher dimensions. In fact, the generalization of the parallelogram equality to higher dimensions is known as the parallelepiped equality. It states that the sum of the squares of the lengths of any set of vectors in an inner product space is equal to the sum of the squares of the lengths of their projections onto a subspace. This generalization is important in solving problems in multi-dimensional geometry and physics.

What are some applications of parallelogram equality in inner product spaces?

The parallelogram equality has many applications in various fields of science and engineering. Some examples include using it to define the notion of orthogonality in quantum mechanics, using it to solve problems in geometry and physics, and using it to prove the Cauchy-Schwarz inequality. It also has applications in computer vision, signal processing, and machine learning, among others.

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