Bounding the p-norms on l_p sequence subspaces

In summary, the problem at hand involves finding an upper bound for the r-norm of a sequence in a space where the series of the sequence converges. The case for finite spaces is solved, but for infinite spaces, a suitable bounding technique has not been found. The Sandwich theorem can be used to prove the result, but it is necessary to find an upper bound for the r-norm. One approach is to consider the fact that the sum of |x_i|^r converges, and use it to show that the sum of |x_i|^k*r (where k is a positive integer) becomes smaller as r approaches infinity. This allows us to ignore the infinite tail of the sequence and focus on the finite case, similar to
  • #1
ELESSAR TELKONT
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Homework Statement



Let [tex]1\leq r<\infty[/tex] and [tex]x\in\ell_{r}=\left\{ x \text{ is a sequence with } \sum_{n=1}^{\infty}\left\vert x_{n}\right\vert^{r} \text{ converges.}\right\}[/tex], then

[tex]\left\vert\left\vert x\right\vert\right\vert_{\infty}=\lim_{r\rightarrow\infty}\left\vert\left\vert x\right\vert\right\vert_{r}[/tex]

Homework Equations




The Attempt at a Solution



I have already proven that for [tex]1\leq s\leq r\leq\infty[/tex] I can bound below by [tex]\left\vert\left\vert x\right\vert\right\vert_{\infty}\leq \left\vert\left\vert x\right\vert\right\vert_{r}[/tex]. The other part wher I'm stuck is to bound above the r-norm. I haven't found something to do it. Obviously, the case for [tex]\mathbb{R}^{n}[/tex] (the finite case if you considere the sequences case as "infinituples") is solved because you have something to do a bounding almost immediatly since

[tex]\sum_{i=1}^{n}\left\vert x_{n}\right\vert^{r}=\left\vert\left\vert x\right\vert\right\vert_{r}^{r}\leq\sum_{i=1}^{n}\left\vert x_{m}\right\vert^{r}=n\left\vert x_{m}\right\vert^{r}=n\left\vert\left\vert x\right\vert\right\vert_{\infty}^{r}[/tex]

Obviously in sequences case [tex]n[/tex] makes not sense, and approximation like i have done above is not possible because a series of a constant sequence don't converge.

Proving what I can't prove the result of the problem follows immediatly because of the "Sandwich theorem" since if I have some quantity bounded above and below like this

[tex]\left\vert\left\vert x\right\vert\right\vert_{\infty}\leq\left\vert\left\vert x\right\vert\right\vert_{r}\leq c^{\frac{1}{r}}\left\vert\left\vert x\right\vert\right\vert_{\infty}[/tex] with [tex]c>0[/tex] and [tex]c\in\mathbb{R}[/tex]

and if I take the limit when [tex]r\rightarrow\infty[/tex] then

[tex]\left\vert\left\vert x\right\vert\right\vert_{\infty}\leq\lim_{r\rightarrow\infty}\left\vert\left\vert x\right\vert\right\vert_{r}\leq \left\vert\left\vert x\right\vert\right\vert_{\infty}[/tex]

Then I would like you to say some ideas to do bounding above.
 
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  • #2
Since the sum of |x_i|^r converges, there is an N such that |x_i|^r<1/2 for all i>=N. That means |x_i|^(k*r)<(1/2)^(k-1)*|x_i|^r, right? So if A is the sum |x_i|^r for i from N to infinity, the sum of |x_i|^(k*r)<(1/2)^(k-1)*A. Now let k->infinity. I.e. the sum of the infinite tail of the sequence |x_i|^r vanishes as r->infinity. So ignore it. Now it's just like proving the finite case.
 

Related to Bounding the p-norms on l_p sequence subspaces

1. What is the concept of "p-norms" in mathematics?

The p-norm of a vector is a mathematical concept used to measure the magnitude or length of a vector in a specific direction. It is defined as the sum of the absolute values of the vector's coordinates raised to the power of p, and then taking the p-th root of the sum. In simpler terms, it is a way to measure the distance of a vector from the origin in a given direction.

2. How are p-norms used to bound l_p sequence subspaces?

In the study of l_p sequence subspaces, p-norms are used to define the metric space in which these subspaces exist. By bounding the p-norms, we can limit the size and complexity of the subspaces, making them easier to analyze and understand.

3. What are some practical applications of bounding p-norms on l_p sequence subspaces?

Bounding p-norms on l_p sequence subspaces has many real-world applications, such as in data compression, signal processing, and image recognition. By limiting the size of the subspaces, we can reduce the amount of data needed to represent them, making them more efficient and easier to analyze.

4. Are there any limitations to bounding p-norms on l_p sequence subspaces?

While bounding p-norms can be useful in many cases, it is not always the most effective method of analyzing subspaces. In some cases, other metrics may be more suitable for the particular problem at hand. Additionally, bounding p-norms may not always accurately represent the complexity of a subspace, as they only consider the magnitude of the vectors and not their direction.

5. How do researchers determine the optimal bounds for p-norms on l_p sequence subspaces?

The process of determining optimal bounds for p-norms on l_p sequence subspaces involves a combination of mathematical analysis and experimentation. Researchers often use various techniques, such as optimization algorithms, to find the most efficient and accurate bounds for a specific problem. These bounds may also be refined and improved over time as new research and techniques are developed.

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