Proving Normed Vectorspace: K > 0 Solution

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In summary, the conversation discusses proving the existence of a constant K that satisfies a given inequality with respect to the norm of a vector in R^n. The L1-norm is also introduced and the question of whether the right side of the inequality will always be larger than the left side is raised.
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Hummingbird25
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Hi

I'm tasked with proving the following:

Let S be an open interval S and [tex]f: S -> \mathbb{R}^n[/tex] be a continuous function.

Let [tex]|| \cdot ||[/tex] be norm on [tex]\mathbb{R}^n[/tex]. show

1) There exist a K > 0 such that [tex]||x|| \leq K||x||_1 ; \ x \in \mathbb{R}^n, ||x||_1 = \sum_{i=1} ^n |x_i|[/tex].

My Solution:

According to the definition the norm of a vector x in R^n is the non-negative scalar [tex]||x|| = \sqrt{x_1^{2} + x_2^{2} + \cdots x_n^2} [/tex]

The L1-norm can be written as [tex]||x||_1 = |x_1| + |x_2| + \cdots + |x_n|[/tex]

Expanding the inequality:

[tex]\sqrt{x_1^{2} + x_2^{2} + \cdots x_n^2} \leq K|x_1| + K|x_2| + \cdots + K|x_n|[/tex]

Is it then concludable from this that since K>0, then the right side of the inequality sign will always be larger than the left side?

Sincerley Yours
Humminbird25
 
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  • #2
Hummingbird25 said:
Is it then concludable from this that since K>0, then the right side of the inequality sign will always be larger than the left side?

Can you expand on how you think this argument would work?
 

What is a normed vectorspace?

A normed vectorspace is a mathematical concept that describes a set of vectors with a defined norm function. The norm function assigns a non-negative value to each vector, representing its "length" or magnitude. The normed vectorspace must also satisfy certain properties, such as the triangle inequality and the existence of a zero vector.

What does it mean to prove a normed vectorspace?

Proving a normed vectorspace means to show that the set of vectors and the norm function associated with it satisfy all the necessary properties and axioms to be considered a valid normed vectorspace. This involves demonstrating that the norm function is well-defined, and that it satisfies the conditions of a norm (such as being non-negative and obeying the triangle inequality).

What is the role of K in proving a normed vectorspace?

K is a constant value that is used in the definition of a normed vectorspace. Specifically, K > 0 is a condition that must be satisfied for a set of vectors to be considered a normed vectorspace. This condition ensures that the norm function is always positive and that the vectors in the space have a well-defined magnitude.

How do you prove that K > 0 is a solution for a normed vectorspace?

To prove that K > 0 is a solution for a normed vectorspace, you must first show that the norm function is well-defined and satisfies all the necessary properties. Then, you can use mathematical techniques, such as induction or contradiction, to demonstrate that K > 0 is a necessary condition for the normed vectorspace to be valid.

What are some real-world applications of normed vectorspaces?

Normed vectorspaces have many applications in mathematics and physics, such as in the study of vector calculus, geometry, and functional analysis. They are also used in fields such as signal processing, computer graphics, and machine learning. For example, in machine learning, normed vectorspaces are used to represent data points and determine their similarity or distance from one another.

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