What is the Relationship Between Vectors and Normed Linear Spaces?

In summary, we discussed using the triangle inequality to prove that if x=(x1,x2...,xn) is a vector in an n-dimensional vector space, then |xi| <= ||x|| for all i=1,2...,n in any norm. We also explored using orthogonality and inner product properties to prove this in the finite dimensional case.
  • #1
Oster
85
0
I'm trying to do a problem concerning converging sequences in normed linear spaces. Can anyone help me prove that if x=(x1,x2...,xn) is a vector in an n dimensional vector space then |xi| where i=1,2...,n; is always less than or equal to ||x|| (norm of x). Maybe start out by writing x as a sum of n multiples of the basis vectors?
 
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  • #2
its not quite true, should be less than or equal..

is this the usual norm or just a norm in general?
 
  • #3
yeah, i forgot to put in the "or equal to". It is any norm in general.
 
  • #4
so what do you know about a norm that may help?

in particular, i would look at the triangle inequality
 
  • #5
Oster said:
I still can't see it.
Triangle inequality -> ||a+b|| <= ||a|| + ||b||
and ||x-y|| <= ||x-z|| + ||y-z||

how about considering
x1 = (x1,0,0,..) and
x = x1 + u
or maybe even better
x1 = x+(-u)
 
  • #6
uhhh
So I have ||x1|| = |x1| <= ||x|| + ||u||
 
  • #7
I still don't see how the triangle inequality implies this =/. I think I proved it using orthogonality and inner product properties in the finite dimensional case.

Assume the negation is true. There exists a non-zero vector x=(x1,x2...xn) such that
|x1| > ||x|| writing out with an orthonormal basis, we get ||x1e1|| > ||x1e1...+xnen||.
On squaring, we'd get x1^2 > x1^2 +... + xn^2 whiich is a contradiction?
 
  • #8
so this assumes the standard norm - is that ok?
 

1. What is the norm of a vector?

The norm of a vector is a mathematical concept used to measure the length or magnitude of a vector. It is denoted by ||v|| and is also known as the magnitude or absolute value of a vector.

2. How is the norm of a vector calculated?

The norm of a vector can be calculated using the Pythagorean theorem, which states that the square of the length of the hypotenuse of a right triangle is equal to the sum of the squares of the lengths of the other two sides. In the case of a vector, the length of the hypotenuse is the norm, and the length of the other two sides are the components of the vector.

3. What is the difference between the norm of a vector and its magnitude?

The norm of a vector is the same as its magnitude. They both refer to the length or magnitude of a vector and are used interchangeably.

4. Why is the norm of a vector important?

The norm of a vector is important in many areas of mathematics and science. It is used to calculate distances, find the direction of a vector, and to determine whether two vectors are perpendicular or parallel to each other. It also plays a crucial role in many algorithms and equations.

5. Can the norm of a vector be negative?

No, the norm of a vector is always a positive value. This is because it is a measure of length and length cannot be negative. If a vector has a negative component, it will contribute to the overall magnitude of the vector, making the norm positive.

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