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Prove that $$ |\rho(y, z)-\rho(x, z)| \leq \rho(x, y) $$ in any metric space \((S, \rho)\).

Short Answer

Expert verified
Using the triangle inequality and rearrangements, we show \(|\rho(y, z) - \rho(x, z)| \leq \rho(x, y)\).

Step by step solution

01

Recall the Triangle Inequality

In any metric space, the triangle inequality states that for any three points \(x, y, z \in S\), the inequality \( \rho(x, z) \leq \rho(x, y) + \rho(y, z) \) holds. This axiom will be critical for our proof.
02

Apply the Triangle Inequality

Use the triangle inequality for the pair of points \(y, z\) and \(x\) with respect to the metric \(\rho\). This gives us: \( \rho(y, z) \leq \rho(y, x) + \rho(x, z) \).
03

Rearrange the Inequality

Rearrange the inequality from Step 2: \( \rho(y, z) - \rho(x, z) \leq \rho(x, y) \). This is one of the two inequalities we need to consider.
04

Consider the Reverse Triangle Inequality

Now consider the triangle inequality in reverse: start with \( \rho(x, z) \leq \rho(x, y) + \rho(y, z) \). This can be rearranged to \( \rho(x, z) - \rho(y, z) \leq \rho(x, y) \).
05

Recognize the Absolute Value Property

Notice that the inequality \( |a - b| \leq c \) is equivalent to \( -c \leq a - b \leq c \). Use this property to conclude that \( |\rho(y, z) - \rho(x, z)| \leq \rho(x, y) \) since both inequalities derived in Steps 3 and 4 satisfy it.

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Key Concepts

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

Triangle Inequality
The triangle inequality is a fundamental concept in metric spaces. It tells us that for any three points \(x\), \(y\), and \(z\) in a space \(S\) with metric \(\rho\), the distance between any two points is always less than or equal to the sum of the distances of those two points to a third point. In mathematical terms, this is expressed as:\[\rho(x, z) \leq \rho(x, y) + \rho(y, z)\]This property is crucial for understanding distances and can be visualized like a triangle in geometry, hence the name. The idea is that the shortest path between two points is always a direct path, not a detour through a third point.
  • It assures that direct moves are efficient. Think of traveling from your home to a store not by stopping at a friend's house but directly.
  • Creates a relation of distance consistency in any space, be it real-world distances or more abstract spaces like function spaces.
Absolute Value Property
The absolute value property can be applied to inequalities to demonstrate distance relations more flexibly. When we say \(|a-b|\), we mean the distance between \(a\) and \(b\), specifically how far apart they are regardless of direction. In the context of inequalities, it means:
  • The expression \(|a-b| \leq c\) is the same as saying \(-c \leq a-b \leq c\).
  • This property allows us to consider both positive and negative differences between values in a symmetric manner.
  • It simplifies the representation of ranges of distances.
For example, in our metric space proof, it allows us to express the inequality \(|\rho(y, z) - \rho(x, z)| \leq \rho(x, y)\) succinctly, combining the two separate but related inequalities derived earlier regarding direct and reverse scenarios.
Reverse Triangle Inequality
The reverse triangle inequality is closely linked to the standard triangle inequality but considers alternative point sequences. In other words, instead of measuring the straightforward path, we consider the potential detour and its bounds in the opposite way.Here's how it works in a metric space:\[|\rho(x, z) - \rho(y, z)| \leq \rho(x, y)\]This suggests that the difference in distances from a point \(z\) to \(x\) and \(y\) doesn't exceed the direct distance between \(x\) and \(y\). This concept is vital in nuanced distance calculations where direct visual interpretation might fail, ensuring consistency.
  • Prevents exaggeration of indirect route benefits, enforcing realistic limits.
  • Such inequalities keep the measure of uncertainty between distances realistic and confined.
In our exercise, this inequality balances out the metrics when rearranging the terms in the proof to demonstrate the said absolute value relation.

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Most popular questions from this chapter

Find \(\sup x_{n}, \inf x_{n}, \max x_{n},\) and \(\min x_{n}\) (if any) for sequences with general term (a) \(n ;\) (b) \((-1)^{n}\left(2-2^{2-n}\right)\); (c) \(1-\frac{2}{n} ;\) (d) \(\frac{n(n-1)}{(n+2)^{2}}\). Which are bounded in \(E^{1} ?\)

Prove that the perpendicular distance of a point \(\bar{p}\) to a plane \(\vec{u} \cdot \bar{x}=c\) in \(E^{n}\) is $$ \rho\left(\bar{p}, \bar{x}_{0}\right)=\frac{|\vec{u} \cdot \bar{p}-c|}{|\vec{u}|} . $$ \(\left(\bar{x}_{0}\right.\) is the orthogonal projection of \(\bar{p},\) i.e., the point on the plane such that \(\left.\overrightarrow{p x_{0}} \| \vec{u} .\right)\)

Compute (a) \(\frac{1+2 i}{3-i}\) (b) \((1+2 i)(3-i) ;\) and (c) \(\frac{x+1+i}{x+1-i}, x \in E^{1}\). Do it in two ways: (i) using definitions only and the notation \((x, y)\) for \(x+y i\); and (ii) using all laws valid in a field.

Prove the additivity of the volume of intervals, namely, if \(A\) is subdivided, in any manner, into \(m\) mutually disjoint subintervals \(A_{1}, A_{2}, \ldots, A_{m}\) in \(E^{n}\), then $$ v A=\sum_{i=1}^{m} v A_{i} $$ (This is true also if some \(A_{i}\) contain common faces). [Proof outline: For \(m=2\), use Problem 8 Then by induction, suppose additivity holds for any number of intervals smaller than a certain \(m\) \((m>1) .\) Now let $$ A=\bigcup_{i=1}^{m} A_{i} \quad\left(A_{i} \text { disjoint }\right) $$ One of the \(A_{i}\) (say, \(\left.A_{1}=[\bar{a}, \bar{p}]\right)\) must have some edge-length smaller than the corresponding edge-length of \(A\left(\right.\) say \(\left., \ell_{1}\right) .\) Now cut all of \(A\) into $$ P=[\bar{a}, \bar{d}] \text { and } Q=A-P \text { (Figure 4) } $$ by the plane \(x_{1}=c\left(c=p_{1}\right)\) so that \(A_{1} \subseteq P\) while \(A_{2} \subseteq Q\). For simplicity, assume that the plane cuts each \(A_{i}\) into two subintervals \(A_{i}^{\prime}\) and \(A_{i}^{\prime \prime}\). (One of them may be empty.) Then $$ P=\bigcup_{i=1}^{m} A_{i}^{\prime} \text { and } Q=\bigcup_{i=1}^{m} A_{i}^{\prime \prime} $$ Actually, however, \(P\) and \(Q\) are split into fewer than \(m\) (nonempty) intervals since \(A_{1}^{\prime \prime}=\emptyset=A_{2}^{\prime}\) by construction. Thus, by our inductive assumption, $$ v P=\sum_{i=1}^{m} v A_{i}^{\prime} \text { and } v Q=\sum_{i=1}^{m} v A_{i}^{\prime \prime} $$ where \(v A_{1}^{\prime \prime}=0=v A_{2}^{\prime},\) and \(v A_{i}=v A_{i}^{\prime}+v A_{i}^{\prime \prime}\) by Problem \(8 .\) Complete the inductive proof by showing that $$ \left.v A=v P+v Q=\sum_{i=1}^{m} v A_{i} .\right] $$

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