# Orthogonality

In mathematics,

**orthogonality**is the generalization of the notion of perpendicularity to the linear algebra of bilinear forms. Two elements

*u*and

*v*of a vector space with bilinear form

*B*are

**orthogonal**when. Depending on the bilinear form, the vector space may contain nonzero self-orthogonal vectors. In the case of function spaces, families of orthogonal functions are used to form a basis.

By extension, orthogonality is also used to refer to the separation of specific features of a system. The term also has specialized meanings in other fields including art and chemistry.

## Etymology

The word comes from the Greek*ὀρθός*, meaning "upright", and

*γωνία*, meaning "angle".

The ancient Greek ὀρθογώνιον

*orthogōnion*and classical Latin

*orthogonium*originally denoted a rectangle. Later, they came to mean a right triangle. In the 12th century, the post-classical Latin word

*orthogonalis*came to mean a right angle or something related to a right angle.

## Mathematics and physics

### Definitions

- In geometry, two Euclidean vectors are
**orthogonal**if they are perpendicular,*i.e.*, they form a right angle. - Two vectors,
*x*and*y*, in an inner product space,*V*, are*orthogonal*if their inner product is zero. This relationship is denoted. - Two vector subspaces,
*A*and*B*, of an inner product space*V*, are called**orthogonal subspaces**if each vector in*A*is orthogonal to each vector in*B*. The largest subspace of*V*that is orthogonal to a given subspace is its orthogonal complement. - Given a module
*M*and its dual*M*^{∗}, an element*m*′ of*M*^{∗}and an element*m*of*M*are*orthogonal*if their natural pairing is zero, i.e.. Two sets and are orthogonal if each element of*S*′ is orthogonal to each element of*S*. - A term rewriting system is said to be orthogonal if it is left-linear and is non-ambiguous. Orthogonal term rewriting systems are confluent.

**pairwise orthogonal**if each pairing of them is orthogonal. Such a set is called an

**orthogonal set**.

In certain cases, the word

*normal*is used to mean

*orthogonal*, particularly in the geometric sense as in the normal to a surface. For example, the

*y*-axis is normal to the curve at the origin. However,

*normal*may also refer to the magnitude of a vector. In particular, a set is called orthonormal if it is an orthogonal set of unit vectors. As a result, use of the term

*normal*to mean "orthogonal" is often avoided. The word "normal" also has a different meaning in probability and statistics.

A vector space with a bilinear form generalizes the case of an inner product. When the bilinear form applied to two vectors results in zero, then they are

**orthogonal**. The case of a pseudo-Euclidean plane uses the term hyperbolic orthogonality. In the diagram, axes x′ and t′ are hyperbolic-orthogonal for any given

*ϕ*.

### Euclidean vector spaces

In Euclidean space, two vectors are orthogonal if and only if their dot product is zero, i.e. they make an angle of 90°, or one of the vectors is zero. Hence orthogonality of vectors is an extension of the concept of perpendicular vectors to spaces of any dimension.The orthogonal complement of a subspace is the space of all vectors that are orthogonal to every vector in the subspace. In a three-dimensional Euclidean vector space, the orthogonal complement of a line through the origin is the plane through the origin perpendicular to it, and vice versa.

Note that the geometric concept of two planes being perpendicular does not correspond to the orthogonal complement, since in three dimensions a pair of vectors, one from each of a pair of perpendicular planes, might meet at any angle.

In four-dimensional Euclidean space, the orthogonal complement of a line is a hyperplane and vice versa, and that of a plane is a plane.

### Orthogonal functions

By using integral calculus, it is common to use the following to define the inner product of two functions*f*and

*g*with respect to a nonnegative weight function

*w*over an interval :

In simple cases,.

We say that functions

*f*and

*g*are

**orthogonal**if their inner product is zero:

Orthogonality of two functions with respect to one inner product does not imply orthogonality with respect to another inner product.

We write the norm with respect to this inner product as

The members of a set of functions are

*orthogonal*with respect to

*w*on the interval if

The members of such a set of functions are

*orthonormal*with respect to

*w*on the interval if

where

is the Kronecker delta.

In other words, every pair of them is orthogonal, and the norm of each is 1. See in particular the orthogonal polynomials.

### Examples

- The vectors
^{T},^{T},^{T}are orthogonal to each other, since + + = 0, + + = 0, and + + = 0. - The vectors
^{T}and^{T}are orthogonal to each other. The dot product of these vectors is 0. We can then make the generalization to consider the vectors in**Z**_{2}^{n}: - The functions and are orthogonal with respect to a unit weight function on the interval from −1 to 1:
- The functions 1, sin, cos :
*n*= 1, 2, 3,... are orthogonal with respect to Riemann integration on the intervals,, or any other closed interval of length 2π. This fact is a central one in Fourier series.#### Orthogonal polynomials

- Various polynomial sequences named for mathematicians of the past are sequences of orthogonal polynomials. In particular:
- *The Hermite polynomials are orthogonal with respect to the Gaussian distribution with zero mean value.
- *The Legendre polynomials are orthogonal with respect to the uniform distribution on the interval.
- *The Laguerre polynomials are orthogonal with respect to the exponential distribution. Somewhat more general Laguerre polynomial sequences are orthogonal with respect to gamma distributions.
- *The Chebyshev polynomials of the first kind are orthogonal with respect to the measure
- *The Chebyshev polynomials of the second kind are orthogonal with respect to the Wigner semicircle distribution.
#### Orthogonal states in quantum mechanics

- In quantum mechanics, a sufficient condition that two eigenstates of a Hermitian operator, and, are orthogonal is that they correspond to different eigenvalues. This means, in Dirac notation, that if and correspond to different eigenvalues. This follows from the fact that Schrödinger's equation is a Sturm–Liouville equation or that observables are given by hermitian operators.
## Art

## Computer science

Orthogonality in programming language design is the ability to use various language features in arbitrary combinations with consistent results. This usage was introduced by Van Wijngaarden in the design of Algol 68:

The number of independent primitive concepts has been minimized in order that the language be easy to describe, to learn, and to implement. On the other hand, these concepts have been applied “orthogonally” in order to maximize the expressive power of the language while trying to avoid deleterious superfluities.

Orthogonality is a system design property which guarantees that modifying the technical effect produced by a component of a system neither creates nor propagates side effects to other components of the system. Typically this is achieved through the separation of concerns and encapsulation, and it is essential for feasible and compact designs of complex systems. The emergent behavior of a system consisting of components should be controlled strictly by formal definitions of its logic and not by side effects resulting from poor integration, i.e., non-orthogonal design of modules and interfaces. Orthogonality reduces testing and development time because it is easier to verify designs that neither cause side effects nor depend on them.

An instruction set is said to be orthogonal if it lacks redundancy and is designed such that instructions can use any register in any addressing mode. This terminology results from considering an instruction as a vector whose components are the instruction fields. One field identifies the registers to be operated upon and another specifies the addressing mode. An orthogonal instruction set uniquely encodes all combinations of registers and addressing modes.

## Communications

In communications, multiple-access schemes are orthogonal when an ideal receiver can completely reject arbitrarily strong unwanted signals from the desired signal using different basis functions. One such scheme is TDMA, where the orthogonal basis functions are nonoverlapping rectangular pulses.Another scheme is orthogonal frequency-division multiplexing, which refers to the use, by a single transmitter, of a set of frequency multiplexed signals with the exact minimum frequency spacing needed to make them orthogonal so that they do not interfere with each other. Well known examples include versions of 802.11 Wi-Fi; WiMAX; ITU-T G.hn, DVB-T, the terrestrial digital TV broadcast system used in most of the world outside North America; and DMT, the standard form of ADSL.

In OFDM, the subcarrier frequencies are chosen so that the subcarriers are orthogonal to each other, meaning that crosstalk between the subchannels is eliminated and intercarrier guard bands are not required. This greatly simplifies the design of both the transmitter and the receiver. In conventional FDM, a separate filter for each subchannel is required.

## Statistics, econometrics, and economics

When performing statistical analysis, independent variables that affect a particular dependent variable are said to be orthogonal if they are uncorrelated, since the covariance forms an inner product. In this case the same results are obtained for the effect of any of the independent variables upon the dependent variable, regardless of whether one models the effects of the variables individually with simple regression or simultaneously with multiple regression. If correlation is present, the factors are not orthogonal and different results are obtained by the two methods. This usage arises from the fact that if centered by subtracting the expected value, uncorrelated variables are orthogonal in the geometric sense discussed above, both as observed data and as random variables.One econometric formalism that is alternative to the maximum likelihood framework, the Generalized Method of Moments, relies on orthogonality conditions. In particular, the Ordinary Least Squares estimator may be easily derived from an orthogonality condition between the explanatory variables and model residuals.

## Taxonomy

In taxonomy, an orthogonal classification is one in which no item is a member of more than one group, that is, the classifications are mutually exclusive.## Combinatorics

In combinatorics, two*n*×

*n*Latin squares are said to be orthogonal if their superimposition yields all possible

*n*

^{2}combinations of entries.

## Chemistry and biochemistry

In synthetic organic chemistry orthogonal protection is a strategy allowing the deprotection of functional groups independently of each other. In chemistry and biochemistry, an orthogonal interaction occurs when there are two pairs of substances and each substance can interact with their respective partner, but does not interact with either substance of the other pair. For example, DNA has two orthogonal pairs: cytosine and guanine form a base-pair, and adenine and thymine form another base-pair, but other base-pair combinations are strongly disfavored. As a chemical example, tetrazine reacts with transcyclooctene and azide reacts with cyclooctyne without any cross-reaction, so these are mutually orthogonal reactions, and so, can be performed simultaneously and selectively. Bioorthogonal chemistry refers to chemical reactions occurring inside living systems without reacting with naturally present cellular components. In supramolecular chemistry the notion of orthogonality refers to the possibility of two or more supramolecular, often non-covalent, interactions being compatible; reversibly forming without interference from the other.In analytical chemistry, analyses are "orthogonal" if they make a measurement or identification in completely different ways, thus increasing the reliability of the measurement. This is often required as a part of a new drug application.