Proof Norm |x_i| ≤ ||x|| for All x ∈ ℝⁿ

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Hence, it follows that |x_i|\leq ||x|| for any x\in\mathbb{R}^n.In summary, using the properties of the scalar product and norm, it can be shown that for any x\in\mathbb{R}^n, |x_i|\leq ||x||, thus proving the statement \left\vert x_{i}\right\vert\leq\left\vert\left\vert x\right\vert\right\vert \forall x\in\mathbb{R}^{n} with the usual scalar product and norm.
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ELESSAR TELKONT
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I have the next problem. I have to proof that [itex]\left\vert x_{i}\right\vert\leq\left\vert\left\vert x\right\vert\right\vert[/itex] [itex]\forall x\in\mathbb{R}^{n}[/itex] with the usual scalar product and norm.

It's obvious that [itex]x_{i}^{2}=\left\vert x_{i}\right\vert^{2}\leq \max\left\{\left\vert x_{i}\right\vert\mid i=1,\dots, n\right\}^{2}=M^{2},\; \forall i[/itex]. Then we have [itex]M^{2}\leq\left\vert\left\vert x\right\vert\right\vert^{2}=\sum_{i=1}^{n}x_{i}^{2}\leq nM^{2}[/itex]. Because of this

[itex]\left\vert x_{i}\right\vert^{2}\leq M^{2}\leq\left\vert\left\vert x\right\vert\right\vert^{2}=\sum_{i=1}^{n}x_{i}^{2}\leq nM^{2}[/itex] and making square root.

[itex]\left\vert x_{i}\right\vert\leq M\leq\left\vert\left\vert x\right\vert\right\vert=\sqrt{\sum_{i=1}^{n}x_{i}^{2}}\leq \sqrt{n}M[/itex]

Is this proof correct? or are something missing or that lacks of justification?
 
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  • #2
How about simply:

[tex]||x||^2=\sum_{j=1}^n x_j^2\geq x_i^2[/tex] (for any i)

[tex]\Leftrightarrow ||x||\geq \sqrt{x_i^2}=|x_i|[/tex]
 

1. What is the "Proof Norm" in mathematics?

The Proof Norm is a concept used in mathematics to measure the size or magnitude of a vector. It is often denoted as ||x|| and is used to calculate the distance between two points or the magnitude of a vector in a multi-dimensional space.

2. How is the Proof Norm calculated?

The Proof Norm is calculated by taking the square root of the sum of the squares of each component of a vector. In other words, ||x|| = √(x₁² + x₂² + ... + xₙ²).

3. What is the significance of |x_i| ≤ ||x|| for All x ∈ ℝⁿ?

This inequality states that the absolute value of any individual component of a vector is always less than or equal to the magnitude of the entire vector. This is a fundamental property of vectors in mathematics and is used in many different applications.

4. How does this proof impact the study of vectors and linear algebra?

This proof is a fundamental result in the study of vectors and linear algebra. It provides a clear understanding of the properties of vectors and how they behave in multi-dimensional spaces. It is also used in many other areas of mathematics, such as differential equations and optimization.

5. Are there any real-world applications of this proof?

Yes, there are many real-world applications of this proof, especially in fields such as physics, engineering, and computer science. It is used in the calculation of forces and velocities, signal processing, and data analysis, among others.

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