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Let \(\|\cdot\|_{1},\|\cdot\|_{2}\) be two norms on a vector space \(X\). Let \(B_{1}\) and \(B_{2}\) be the closed unit balls of \(\left(X,\|\cdot\|_{1}\right)\) and \(\left(X,\|\cdot\|_{2}\right)\), respectively. Prove that \(\|\cdot\|_{1} \leq C\|\cdot\|_{2}\) (that is, \(\|x\|_{1} \leq C\|x\|_{2}\) for all \(x \in X\) ) if and only if \(\frac{1}{C} B_{2} \subset B_{1}\)

Short Answer

Expert verified
\( \left\|\cdot\right\|_{1} \leq C\left\|\cdot\right\|_{2}\) if and only if \( \frac{1}{C}B_{2} \subset B_{1}\).

Step by step solution

01

Define the Problem

The problem requires proving a relationship between two norms, \( \left\|\cdot\right\|_{1} \) and \( \left\|\cdot\right\|_{2} \), on a vector space \(X\). We need to show that \( \left\|\cdot\right\|_{1} \leq C\left\|\cdot\right\|_{2}\) for some constant \(C\) if and only if \( \frac{1}{C} B_{2} \subset B_{1}\). Here, \(B_{1}\) and \(B_{2}\) represent the closed unit balls for the norms \( \left\|\cdot\right\|_{1} \) and \( \left\|\cdot\right\|_{2} \), respectively.
02

Proving the Forward Implication

We start by assuming \( \left\|\cdot\right\|_{1} \leq C\left\|\cdot\right\|_{2}\) for all \(x \in X\). We need to show that \( \frac{1}{C}B_{2} \subset B_{1}\). Take any element \(y\) from \( \frac{1}{C}B_{2}\). This implies that \(Cy \in B_{2}\). By definition, \( \left\|Cy\right\|_{2} \leq 1\). Thus \( \left\|y\right\|_{2} \leq \frac{1}{C}\). Now using the assumed relation, \( \left\|y\right\|_{1} \leq C \left\|y\right\|_{2} \leq C \times \frac{1}{C} = 1\). Hence, \(y \in B_{1}\). This proves the implication.
03

Proving the Reverse Implication

Now assume that \( \frac{1}{C}B_{2} \subset B_{1}\). We need to show that \( \left\|\cdot\right\|_{1} \leq C\left\|\cdot\right\|_{2}\). Take any vector \(x \in X\). Consider the vector \( \frac{x}{\left\|x\right\|_{2}}\) which lies in \(B_{2}\). By the assumption, \( \frac{1}{C} \left( \frac{x}{\left\|x\right\|_{2}} \right) \in B_{1}\). Hence, \( \left\|\frac{1}{C} \frac{x}{\left\|x\right\|_{2}}\right\|_{1} \leq 1 \), which simplifies to \( \left( \frac{1}{C} \right) \left( \frac{\left\|x\right\|_{1}}{\left\|x\right\|_{2}} \right) \leq 1\), or \( \left\|x\right\|_{1} \leq C\left\|x\right\|_{2}\). This proves the reverse implication.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

vector space
A vector space is a collection of vectors, where vectors can be added together and multiplied by scalars (which are usually real or complex numbers). The key properties of a vector space include:
  • Vector addition: For any two vectors \(u\) and \(v\) in the vector space, their sum \(u + v\) is also in the vector space.

  • Scalar multiplication: For any scalar \(a\) and vector \(u\) in the vector space, the product \(au\) is also in the vector space.

These properties must satisfy certain axioms, such as commutativity and associativity of addition, existence of an additive identity (zero vector), and distributivity of scalar multiplication over vector addition. Understanding vector spaces helps in grasping more complex concepts in linear algebra and functional analysis.
unit ball
A unit ball in a normed vector space is the set of all vectors whose norm is less than or equal to 1. Formally, in the space \((X, \|\cdot\|)\), the unit ball is defined as:

\[ B = \{ x \in X : \|x\| \leq 1 \} \]
Unit balls play a crucial role in visualizing and understanding the geometric properties of norms. They help illustrate how different norms identify distances and lengths within a vector space, providing a sense of the 'shape' of the space under different norms.
norm inequality
The norm inequality states that one norm can be bounded by a constant times another norm. Formally, given two norms \(\|\cdot\|_1\) and \(\|\cdot\|_2\), the norm inequality is:

\[ \|x\|_1 \leq C \|x\|_2 \text{ for all } x \in X \]
Here, \(C\) is a constant. This inequality is fundamental in comparing different norms and understanding their relationships. It shows that the size or length of vectors as measured by \(\|\cdot\|_1\) is consistently within a factor of \(C\) times their size as measured by \(\|\cdot\|_2\).
closed unit ball
The closed unit ball in a normed vector space is defined similarly to the unit ball, but it includes all vectors with norm less than or equal to 1. Mathematically, for a norm \(\|\cdot\|\) on \(X\), the closed unit ball is:
\[ B = \{ x \in X : \|x\| \leq 1 \} \]
Closed unit balls are 'closed' in the topological sense, meaning they contain all their boundary points. They are important in studying compactness, continuity, and convergence in normed spaces.
norms
A norm is a function that assigns a non-negative length or size to each vector in a vector space. Formally, a norm \(\|\cdot\|\) on a vector space \(X\) is a function \(\|\cdot\|: X \to [0, \infty)\) satisfying the following properties:
  • Positive definiteness: \(\|x\| = 0\) if and only if \(x = 0\).

  • Homogeneity: For all scalars \(\alpha\) and all vectors \(x\), \(\| \alpha x \| = |\alpha| \|x\|\).

  • Triangle inequality: For all vectors \(x\) and \(y\), \(\|x + y\| \leq \|x\| + \|y\|\).

Understanding norms is essential for studying the structure and properties of vector spaces, particularly in functional analysis and other areas of mathematics.

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Most popular questions from this chapter

Let \(\Gamma\) be a set and \(p \in[1, \infty]\). Show that \(c_{0}(\Gamma)\) and \(\ell_{p}(\Gamma)\) are Banach spaces.

Let \(X\) be a Banach space. Show that if \(A \subset X\) is totally bounded, then there is a sequence \(\left\\{x_{n}\right\\} \in X\) such that \(x_{n} \rightarrow 0\) in \(X\) and \(A \subset\) sconv \(\left\\{x_{n}\right\\}\) (see Exercise \(1.7)\) In particular, for every compact subset \(A\) of \(X\) there exists a sequence \(\left\\{x_{n}\right\\}\) such that \(x_{n} \rightarrow 0\) and \(A \subset \overline{\operatorname{conv} v}\left\\{x_{n}\right\\}\) (Grothendieck). Hint: We set \(A_{1}=A\), let \(B_{1}\) be a finite \(2^{-2}\) -net in \(A_{1}\). If \(A_{i}\) and \(B_{i}\) were defined for \(i \leq n\), let \(A_{n+1}=\left(A_{n}-B_{n}\right) \cap 2^{-2 n} B_{X} ;\) note that every \(a_{n} \in A_{n}\) is of the form \(a_{n}=a_{n+1}+b_{n}\), where \(a_{n+1} \in A_{n+1}, b_{n} \in B_{n}\). Let \(B_{n+1}\) be a finite \(2^{-2 n}\) -net in \(A_{n+1}\). Therefore, every element \(a \in A=A_{1}\) is of the form \(a=b_{1}+a_{2}=b_{1}+b_{2}+a_{3}=\ldots=\sum_{1}^{n} b_{n}+a_{n+1} .\) Since \(a_{n+1} \in 2^{-2 n} B_{X_{1}}\)

Let \(T\) be a one-to-one bounded linear operator from a normed space \(X\) into a normed space \(Y\). Show that \(T\) is an isometry onto \(Y\) if and only if \(T\left(B_{X}\right)=B_{Y}\) if and only if \(T\left(S_{X}\right)=S_{Y}\) if and only if \(T\left(B_{X}^{O}\right)=B_{Y}^{O}\), where \(B_{X}^{O}\) is the open unit ball in \(X\). Hint: By homogeneity, \(T\) is an isometry onto \(Y\) if and only if \(T\left(S_{X}\right)=S_{Y}\). Assume that \(T\left(B_{X}\right)=B_{Y}\). If there is \(x \in S_{X}\) such that \(\|T(x)\|=C<1\) then \(\|x / C\|>1\) and \(\|T(x / C)\|=1 .\) But there must be \(y \in B_{X}\) such that \(T(y)=T(x / C)\), a contradiction with \(T\) being one-to-one.

Let \(X\) be an infinite-dimensional Banach space. Show that \(X\) admits no countable Hamel (algebraic) basis. Therefore, \(c_{00}\) cannot be normed to become a Banach space. Hint: If \(\left\\{e_{i}\right\\}\) is a countable infinite Hamel basis of a Banach space \(X\), put \(F_{n}=\operatorname{span}\left\\{e_{1}, \ldots, e_{n}\right\\} . F_{n}\) are closed and thus, by the Baire category theorem, at least one \(F_{n_{0}}\) has a nonempty interior; that is, there is \(x \in X\) and a ball \(B=\delta B_{X}\) such that \(x+B \subset F_{n_{0}} .\) Using linearity of \(F_{n_{0}}\), we have that \(-x+B \subset F_{n_{0}}\), so \(B \subset(x+B)+(-x+B) \subset F_{n_{0}} .\) Thus 0 is an interior point of \(F_{n_{0}} .\) This would mean that \(F_{n_{0}}=X\), a contradiction.

Let \(\sum x_{i}\) be a series in a Banach space \(X, x \in X\). Show that the following are equivalent: (i) For every \(\varepsilon>0\), there is a finite set \(F \subset \mathbf{N}\) such that \(\left\|x-\sum_{i \in F^{\prime}} x_{i}\right\|<\varepsilon\) whenever \(F^{\prime}\) is a finite set in \(\mathbf{N}\) satisfying \(F^{\prime} \supset F\). (ii) If \(\pi\) is any permutation of \(\mathbf{N}\), then \(\sum x_{\pi(i)}=x\). If these conditions hold, we say that the series is unconditionally convergent to \(x .\) As in the real case, a series \(\sum x_{i}\) is unconditionally convergent if it is absolutely convergent: \(\left\|\sum_{i \in G} x_{i}\right\| \leq \sum_{i=n}^{\infty}\left\|x_{i}\right\|\) for \(G \subset \mathbf{N}\) with \(n \leq \min (G)\). Note that the unconditional convergence of a series does not in general imply its absolute convergence; consider \(\sum \frac{1}{i} e_{i}\) in \(\ell_{2}\). Hint: (i) \Longrightarrow (ii): For every \(\varepsilon>0\), get a finite \(F\) such that \(\left\|\sum_{F^{\prime}} x_{i}-x\right\|<\varepsilon\) whenever \(F^{\prime}\) is a finite set in \(\mathbf{N}\) such that \(F^{\prime} \supset F\). For some \(n_{0}\), we get \(\left\\{\pi(1), \pi(2), \ldots, \pi\left(n_{0}\right)\right\\} \supset F .\) Thus \(\left\|x-\sum_{i=1}^{n} x_{\pi(i)}\right\|<\varepsilon\) for \(n \geq n_{0}\) (ii) \(\Longrightarrow\) (i): By contradiction, get by induction a sequence \(n_{k}\) such that \(\left\|\sum_{i=1}^{n_{k}} x_{i}-x\right\|<\frac{1}{k}\) and a sequence of finite sets \(M_{k}\) such that \(n_{k+1}>\) \(\max \left(M_{k}\right) \geq \min \left(M_{k}\right)>n_{k}\) in such a way that \(\left\|\sum_{i=1}^{n_{k}} x_{i}+\sum_{M_{k}} x_{i}-x\right\| \geq \varepsilon\) Indeed, since (i) fails for \(F=\left\\{1, \ldots, n_{k}\right\\}\), we find \(F^{\prime}\) and set \(M_{k}=F^{\prime} \backslash F\) Find a permutation \(\pi\) such that \(\pi\left(\left\\{1, \ldots, n_{k}+\left|M_{k}\right|\right\\}\right)=\left\\{1, \ldots, n_{k}\right\\} \cup M_{k}\) then \(\sum x_{\pi(i)}\) does not converge. To see that \(\sum \frac{1}{i} e_{i}\) converges unconditionally in \(\ell_{2}\), note that \(\left\|\sum_{G} \frac{1}{i} e_{i}\right\|\) is small if \(\min (G)\) is large enough.

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