Proving Inequality in Inner Product Spaces

In summary, the conversation is about proving the inequality |<x1,y1> - <x2,y2>| ≤ ||x1 - x2||*||y1|| + ||x2||*||y1-y2|| using applicable axioms of normals and inner products. The hint given is to try subtracting and adding <x2,y1>, and using the Cauchy-Schwartz inequality and the triangle inequality. LCKurtz provided helpful hints and the final solution was achieved by using the first hint to add and subtract <x2,y1> on the left hand side, and using the Cauchy-Schwartz inequality to show that the right hand side is greater than or equal to the left
  • #1
ChickysPusss
13
1

Homework Statement


First I'd like to state the meaning of my notations
x = (x0,x1,x2...xn)
y = (y0,y1,y2...yn)
|x| = absolute value of x
||x|| = Normal of x
<x,y> = Inner Product of x and y

I have to prove the following

|<x1,y1> - <x2,y2>| ≤ ||x1 - x2||*||y1|| + ||x2||*||y1-y2||

Homework Equations



Applicable Axioms of Normals and Inner Products
||x|| = √(<x,x>)
<x + z,y> = <x,y> + <z,y>

The Attempt at a Solution



I tried expanding the right hand side as such:
||x1 - x2|| = √(<x1-x2,x1-x2>) = √(<x1,x1> + 2*<x1,-x2> + <-x2,-x2>)
||x2|| = √(<x2,x2>)

I did similarly for the y values, and I'm not seeing anything that pops out to me as a solution to this proof, nothing seems to cancel, and no axioms seem to make this work in a general sense. I guess what I need...is a HINT.
 
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  • #2
Hint: Try subtracting and adding ##\langle x_2,y_1\rangle##.
 
  • #3
Thank you, but I still don't see how that's applicable, everything's inside an absolute value or a square root so I'm not sure how to get in there? Can I have a double hint?
 
  • #4
Don't you have the Cauchy-Schwartz inequality ##|\langle x,y\rangle|\le \|x\|\|y\|## for an inner product space? That plus the first hint should do it...

[Edit, added] And, of course, the triangle inequality
 
Last edited:
  • #5
LCKurtz there's no easy way to put this, but you are THE MAN. Thank you so much, here's the solution I came to thanks to your hints. I really should've came to it easier, but what can you do.

|<x1,y1> - <x2,y2>| ≤ ||x1 - x2||*||y1|| + ||x2||*||y1-y2||

Using the first hint so graciously given me by LCKurtz, I added and subtracted <x2,y1> on the left hand side.

This gives:
|<x1,y1> - <x2,y1> - <x2,y2> + <x2,y1>|
Then by an axiom listed above, we can bring this to:
|<x1 -x2,y1> + <y1-y2,x2>|

Then working on the right hand side, and using the memory jump-starter so humbly given by Zeus Incarnate, Mr. LCKurtz (Talking about the Cauchy-Schwartz inequality).
||x1-x2||*||y1|| >= |<x1-x2,y1>|
||y1-y2||*||x2|| >= |<y1-y2,x2>|

This means the right hand side as it is is greater than or equal to |<x1-x2,y1> + <y1-y2,x2>|, also known as... DUN DUN DUNNNNN the left hand side! So clearly, this is the end of the proof. A shout out to my boy LCKurtz for his help, thanks.
 

FAQ: Proving Inequality in Inner Product Spaces

What is an inner product space?

An inner product space is a mathematical concept that describes a vector space where an inner product operation is defined. This inner product operation takes two vectors as inputs and produces a scalar value as an output. It is used to measure the angle between two vectors and the length of a vector.

How do you prove that a vector space is an inner product space?

To prove that a vector space is an inner product space, you need to show that it satisfies certain properties. These properties include linearity, symmetry, and positive definiteness. Linearity means that the inner product operation is distributive and follows the rules of scalar multiplication. Symmetry means that switching 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 positive.

What is the importance of inner product spaces in mathematics?

Inner product spaces have many applications in mathematics, including linear algebra, functional analysis, and quantum mechanics. They provide a way to measure the similarity between vectors and are used to define important concepts such as orthogonality and projection. Inner product spaces also allow for the use of powerful tools like the Gram-Schmidt process and the concept of adjoints.

Can any vector space be an inner product space?

No, not all vector spaces can be inner product spaces. For a vector space to be an inner product space, it must satisfy the properties mentioned earlier. If these properties are not satisfied, then the vector space cannot be an inner product space. For example, a vector space with only one vector cannot have an inner product defined.

How do you use inner product spaces to prove the Cauchy-Schwarz inequality?

The Cauchy-Schwarz inequality states that the absolute value of the inner product of two vectors is less than or equal to the product of their lengths. To prove this using inner product spaces, we use the properties of linearity and positive definiteness. By taking the inner product of two arbitrary vectors and applying these properties, we can show that the result is always less than or equal to the product of the lengths of the vectors.

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