Cauchy-Schwarz Inequality

In summary, the Cauchy-Schwarz Inequality states that for two n-dimensional vectors v1 and v2, |\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|\|\mathbf{v}_2\| or |\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|+\|\mathbf{v}_2\|, with equality holding only when one vector is a multiple of the other. The constant "a" in the equality can be any scalar for vector spaces over any ordered field.
  • #1
EngWiPy
1,368
61
Hello,

For two n-dimensional vectors [tex]\mathbf{v}_1\text{ and }\mathbf{v}_2[/tex], what is the Cauchy-Schwarz Inequality:

1- [tex]|\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|\|\mathbf{v}_2\|[/tex], or

2- [tex]|\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|+\|\mathbf{v}_2\|[/tex]

In either case, the equality holds when [tex]\mathbf{v}_1=a\,\mathbf{v}_2[/tex], where a is a positive real constant. Is there any specific way to compute a, or just pick an arbitrary positive real number?

Regards
 
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  • #2
I don't believe you can calculate 'a' from the inequality. You would need another route to get it.
 
  • #3
Your second inequality is the "triangle inequality." The Cauchy-Schwarz inequality is [itex]| \langle \mathbf{A},\mathbf{B} \rangle | \leq \|{\mathbf{A}}\| \, \|{\mathbf{B}} \|[/itex]
 
  • #4
the equality holds when v1=av2 for any scalar a.
 
  • #5
The first is the CS inequality.BTW the second inequality has an error (dot instead or plus). It should read :

[tex]|\mathbf{v}_1 + \mathbf{v}_2|\leq\|\mathbf{v}_1\|+\|\mathbf{v}_2\|[/tex]

In which case it's the triangle inequality.
 
  • #6
So, can we write:

[tex]|\mathbf{w}^H\,\mathbf{h}|^2\leq\|\mathbf{w}\|^2\,\|\mathbf{h}\|^2[/tex]?
 
  • #7
S_David said:
Hello,

For two n-dimensional vectors [tex]\mathbf{v}_1\text{ and }\mathbf{v}_2[/tex], what is the Cauchy-Schwarz Inequality:

1- [tex]|\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|\|\mathbf{v}_2\|[/tex], or

2- [tex]|\mathbf{v}_1.\mathbf{v}_2|\leq\|\mathbf{v}_1\|+\|\mathbf{v}_2\|[/tex]

In either case, the equality holds when [tex]\mathbf{v}_1=a\,\mathbf{v}_2[/tex], where a is a positive real constant. Is there any specific way to compute a, or just pick an arbitrary positive real number?

Regards
What they are saying is that equality holds if an only if one vector is a multiple of the other. a could be any real number. It is not a matter of calculating a or a picking a.
 
  • #8
Ok, thank you all guys.
 
  • #9
HallsofIvy said:
What they are saying is that equality holds if an only if one vector is a multiple of the other. a could be any real number.

Could we say, more generally, that a could be any scalar (so that this would hold for any inner product space, over any field)?
 
  • #10
Rasalhague said:
Could we say, more generally, that a could be any scalar (so that this would hold for any inner product space, over any field)?

As far as I know, Cauchy proved the inequality for complex vector spaces and Schwarz proved it for polynomial space. In my general linear algebra text, it is proven for real/complex vector spaces. It is a special case of a http://planetmath.org/encyclopedia/CauchySchwartzInequality.html .
 
Last edited by a moderator:
  • #11
Rasalhague said:
Could we say, more generally, that a could be any scalar (so that this would hold for any inner product space, over any field)?
Over any ordered field, yes. That is necessary in order that we be able to say "[itex]\le[/itex]".
 

1. What is the Cauchy-Schwarz Inequality and why is it important?

The Cauchy-Schwarz Inequality, also known as the Cauchy-Bunyakovsky-Schwarz Inequality, is a fundamental inequality in mathematics that relates to the dot product or inner product of two vectors. It states that for any two vectors, the absolute value of their dot product is less than or equal to the product of their magnitudes. This inequality is important because it has numerous applications in areas such as linear algebra, functional analysis, and probability theory.

2. How is the Cauchy-Schwarz Inequality used in linear algebra?

In linear algebra, the Cauchy-Schwarz Inequality is used to prove other important theorems and inequalities, such as the Triangle Inequality and the Schwarz Inequality. It is also used to establish the existence of an inner product space, which is a vector space equipped with an inner product that satisfies certain properties.

3. Can the Cauchy-Schwarz Inequality be extended to more than two vectors?

Yes, the Cauchy-Schwarz Inequality can be extended to any number of vectors. This is known as the generalized Cauchy-Schwarz Inequality and it states that for any set of n vectors, the absolute value of their dot product is less than or equal to the product of their magnitudes multiplied by the product of the magnitudes of all the other vectors.

4. What is the relationship between the Cauchy-Schwarz Inequality and the Schwarz Inequality?

The Cauchy-Schwarz Inequality is a special case of the Schwarz Inequality, which is a more general inequality that applies to any inner product space. The Schwarz Inequality states that for any two vectors, the absolute value of their inner product is less than or equal to the product of their norms. The Cauchy-Schwarz Inequality is a special case because it only applies to the dot product in Euclidean space.

5. How is the Cauchy-Schwarz Inequality used in probability theory?

In probability theory, the Cauchy-Schwarz Inequality is used to prove other important inequalities, such as the Markov Inequality and the Chebyshev Inequality. These inequalities are used to bound the probability of certain events and to determine the convergence of random variables. The Cauchy-Schwarz Inequality is also used in the proof of the famous Central Limit Theorem.

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