When does equality hold in Cauchy-Schwarz inequality

In summary, the conversation discusses proving that if V is a vector space over \mathbb{C}^n with the standard inner product, then the identity |<x,y>| = ||x|| \cdot ||y|| implies that one of the vectors x or y is a multiple of the other. The attempt at a solution involves finding a proof for this statement, including using the fact that |a| \cdot ||y|| = ||x||. Eventually, it is shown that this leads to the conclusion that ||z|| = 0 and therefore, one of the vectors must be a multiple of the other.
  • #1
Hitman2-2

Homework Statement


Prove that if V is a vector space over [tex] \mathbb{C}^n [/tex] with the standard inner product, then

[tex]
|<x,y>| = ||x|| \cdot ||y||
[/tex]

implies one of the vectors x or y is a multiple of the other.


The Attempt at a Solution


Assume the identity holds and that y is not zero. Let

[tex]
a = \frac {<x,y>} {||y||^2}
[/tex]

and let z = x - ay. I've shown that y and z are orthogonal and want to show

[tex]
|a| = \frac {||x||} {||y||}
[/tex]

Well,

[tex]
|a| \cdot ||y|| = \frac {|<x,y>|} {||y||} = \frac {|\sum_{i=1}^n a_i \overline{b_i} |} {\sqrt{\sum_{i=1}^n |b_i|^2}}
= \sqrt{\frac {\left(\sum_{i=1}^n a_i \overline{b_i} \right) \left(\sum_{i=1}^n \overline{a_i} b_i \right)} {\sum_{i=1}^n |b_i|^2} }
[/tex]

but now I don't see how to simply this further to get this equal to the norm of x.
 
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  • #2
Hitman2-2 said:
[tex]
|a| \cdot ||y|| = \frac {|<x,y>|} {||y||} = \frac {|\sum_{i=1}^n a_i \overline{b_i} |} {\sqrt{\sum_{i=1}^n |b_i|^2}}
= \sqrt{\frac {\left(\sum_{i=1}^n a_i \overline{b_i} \right) \left(\sum_{i=1}^n \overline{a_i} b_i \right)} {\sum_{i=1}^n |b_i|^2} }
[/tex]

but now I don't see how to simply this further to get this equal to the norm of x.

Never mind ... I think I've got it. I've totally neglected that by assumption,

[tex]
\frac { | \langle x,y \rangle | } { \| y \| } = \| x \|
[/tex]

so then it follows that

[tex]
|a| \cdot \| y \| = \| x \|
[/tex]

Then since

[tex]
\| x \|^2 = \| ay + z \|^2 = \| ay \|^2 + \| z \|^2 \Rightarrow \| z \| = 0
[/tex]

the result follows.

Embarassing.
 

What is the Cauchy-Schwarz Inequality?

The Cauchy-Schwarz Inequality is a fundamental mathematical inequality that relates the inner product of two vectors to their magnitudes. It states that for any two vectors \(u\) and \(v\) in a vector space, the following inequality holds:

\[|\langle u, v \rangle|^2 \leq \langle u, u \rangle \cdot \langle v, v \rangle\]

Here, \(\langle u, v \rangle\) represents the inner product (also known as the dot product) of vectors \(u\) and \(v\), and \(|\langle u, v \rangle|\) denotes the absolute value of the inner product.

When Does Equality Hold in the Cauchy-Schwarz Inequality?

Equality in the Cauchy-Schwarz Inequality holds under specific conditions. Equality occurs if and only if the vectors \(u\) and \(v\) are linearly dependent, meaning one vector is a scalar multiple of the other. In mathematical terms:

Equality holds if there exists a scalar \(k\) (where \(k\) is not equal to zero) such that:

\[u = kv\]

Alternatively, you can express this condition as follows:

\[\frac{u}{\|u\|} = \frac{v}{\|v\|}\]

Here, \(\|u\|\) and \(\|v\|\) represent the magnitudes (or lengths) of vectors \(u\) and \(v\), respectively.

In other words, if vectors \(u\) and \(v\) are proportional to each other, or if they point in the same or opposite direction, then equality holds in the Cauchy-Schwarz Inequality. In all other cases, where the vectors are not linearly dependent, the inequality is strict (i.e., equality does not hold).

Why is it important to know when equality holds?

Understanding when equality holds in the Cauchy-Schwarz Inequality is important in various mathematical and practical contexts, including:

1. Optimization:

In optimization problems, the Cauchy-Schwarz Inequality can be used to establish conditions for achieving the maximum or minimum values of certain quantities. Knowing when equality holds helps identify critical points in optimization problems.

2. Vector Spaces:

In linear algebra and vector spaces, recognizing when equality holds is essential for understanding the relationships between vectors. Linearly dependent vectors play a significant role in vector spaces and their properties.

3. Signal Processing:

In signal processing and engineering, the Cauchy-Schwarz Inequality is used to analyze signals and determine when two signals are correlated or orthogonal. Equality in the inequality indicates a specific relationship between signals.

4. Probability and Statistics:

In probability theory and statistics, the Cauchy-Schwarz Inequality is applied to derive inequalities related to covariance, correlation coefficients, and variance. Understanding when equality occurs is relevant in statistical analysis.

Overall, recognizing the conditions for equality in the Cauchy-Schwarz Inequality is valuable for solving mathematical problems and interpreting results in various fields.

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