What is Orthonormal: Definition & Explanation

  • Thread starter Greg Bernhardt
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In summary, a set of vectors is called "orthonormal" if the vectors are normalized to 1 and are orthogonal to each other. This means that their dot product is equal to the Kronecker delta function. Functions can also be considered as vectors with a defined dot product, such as the integral of their product with a weighing function. In the case of trigonometric functions, the orthogonality properties can be expressed using integrals over a full period. These properties are important in understanding Hilbert spaces and their relatives.
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Definition/Summary

A set of vectors
[tex]
\left\{\mathbf{v}^{(i)}\right\}
[/tex]
is called "orthonormal" if the vectors of the set are normalized to 1 and are orthogonal to each other.

[tex]
\mathbf{v}^{(i)}\cdot\mathbf{v}^{(j)}=\delta_{ij}\;,
[/tex]
where [itex]\delta_{ij}[/itex] is the Kronecker delta function.

Equations



Extended explanation

Functions may also be considered as vectors with an appropriately defined dot-product. For example, the dot product for functions of a single variable could be defined as
[tex]
\mathbf{f}\cdot\mathbf{g}\equiv \int_{-\infty}^{\infty} w(x) f^*(x) g(x)dx\;,
[/tex]
where [itex]w(x)[/itex] is an appropriate weighing function. An example where [itex]w(x)[/itex] is a unitstep function on the interval 2p, and where f and g are trig functions is given below.

In what follow, the constants [itex]m[/itex] and [itex]n[/itex] are nonnegative real integers. The orthogonality properties of the trigonometric system are expressed by:

[tex]
\begin{align*}
\int_{ - p + x_0 }^{p + x_0 } {\sin {\frac{m\pi x}{p}}\cos {\frac{n\pi x}{p}} x} &= 0 \quad \text{for all m and n} \\
\int_{ - p + x_0 }^{p + x_0 } {\cos {\frac{m\pi x}{p}} \cos {\frac{n\pi x}{p}} dx} &=
\left\{
\begin{array}{cll}
2p & \text{for}&m=n=0\\
p & \text{for} &m=n>0\\
0 & \text{for} &m\neq n
\end{array}
\right. \\
\int_{ - p + x_0 }^{p + x_0 } {\sin {\frac{m\pi x}{p}} \sin {\frac{n\pi x}{p}} d x} &=
\left\{
\begin{array}{cll}
0 & \text{for}&m=n=0\\
p & \text{for} &m=n>0\\
0 & \text{for} &m\neq n.
\end{array}
\right.
\end{align*}
[/tex]

Here [itex]2p[/itex] is the period, and [itex]x_0[/itex] is an arbitrary constant. We are allowed to add the constant [itex]x_0[/itex] to the limits, since we are integrating over a full period.

* This entry is from our old Library feature. If you know who wrote it, please let us know so we can attribute a writer. Thanks!
 
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Related to What is Orthonormal: Definition & Explanation

1. What does "orthonormal" mean?

"Orthonormal" is a mathematical term that describes a set of vectors that are both orthogonal (perpendicular) and normalized (have a magnitude of 1). This means that all the vectors in the set are perpendicular to each other and have a length of 1.

2. How is orthonormality different from orthogonality?

Orthogonality refers to vectors that are perpendicular to each other, while orthonormality requires both orthogonality and normalization. In other words, all orthonormal vectors are also orthogonal, but not all orthogonal vectors are orthonormal.

3. What is the importance of orthonormality in mathematics and science?

Orthonormality is important in many areas of mathematics and science, including linear algebra, signal processing, and quantum mechanics. It allows for simpler and more efficient calculations and can help identify relationships and patterns in data.

4. How is orthonormality related to the concept of a basis?

An orthonormal set of vectors can be used as a basis for a vector space, meaning that any vector in that space can be represented as a linear combination of the orthonormal vectors. This makes it easier to work with vectors and perform calculations.

5. Can an infinite set of vectors be orthonormal?

Yes, an infinite set of vectors can be orthonormal. However, in practical applications, a finite set of orthonormal vectors is typically used due to limitations in computational resources.

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