Orthonormal, Orthogonal, Perpendicular

In summary, orthoganal and perpendicular are often used interchangeably to mean that the inner product between two vectors is zero. However, in some contexts, perpendicular may specifically refer to a geometric interpretation of the inner product. Orthogonal is applied to linearly independent sets to mean that the inner product is zero for any two vectors in the set that are not the same. Orthonormal extends this concept by also requiring the inner product to be 1 for identical vectors. The term orthogonal originated from the Greek words for "right" and "angle", referring to lines at right angles. This concept can be expanded to vectors and functions in higher dimensions. However, there is a standard misuse of the term in the case of "orthogonal group",
  • #1
Nusc
760
2
What is the difference between these terms?
In what context do they apply to?
How important is it that we treat them differently?
 
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  • #2
Nusc said:
What is the difference between these terms?
In what context do they apply to?
How important is it that we treat them differently?
When speaking of two vectors u,v perpendicular and orthoganal are used interchangably to mean that an inner product is zero.
<u|v>=0.
Perpendicular sometimes, but not always is used to indicate that the inner product in question has geometric interpitations. In that context <u|v> would mean two lines related to the vectors form right angles.
Orthoganal is applied to linearly independent sets to mean that for any two vectors in a set <v(i)|v(j)>=0 if i and j are not the same. Orthonormal means that in addition to being orthoganal <v(i)|v(i)>=1. This is quite use full because the problem of determining the coefficient of a vector in a representation of a vector by a basis is in general dependent on solving a linear system, but reduces in a orthoganal basis to finding an inner product.
because
v=a1v1+a2vi+...
so
<v(i)|v>=a(i)<v(i)|v(i)>
since the other terms are zero.
 
  • #3
orthogonal is from greek: ortho = right, gonal =angle. So I guess primarily, orthogonal was an adjective applying to lines: two lines at right angle with each other are said to be orthogonal. In this context, 'orthogonal' and 'perpendicular' are synomyms. This definition of orthogonality can easily be expanded to vectors in 2 or 3 dimensional space. Two vectors are orthogonal if their graphical representation (the arrows) are at 90° to one another. However, the concept of orthogonality has expended beyong the realm of 3 dimensional geometry. The algebraic (as opposed to geometric) condition for having two 3D vectors (a,b,c) and (d,e,f) to be orthogonal is that ad+be+cf = 0. So the generalisation of the notion of orthogonality to vectors of R^n is obvious: two vectors or R^n (a_1,...,a_n) and (b_1,...,b_n) are said to be orthogonal if a_1b_1+...+a_nb_n = 0. About orthonormality: two vectors are orthonormal if they are orthogonal and their norm is 1.

And the notion of orthogonality goes beyond that of vectors. For exemple, two functions f(x) and g(x) are said to be orthogonal over the interval [a,b] with weighting function w(x) if their inner product, defined as the integral of fgw from a to b, is 0. We also defined orthonormality between functions as "f(x) and g(x) are orthonormal over the interval [a,b] with weighting function w(x) if the integral of fgw from a to b, is 0 if f and g are not equal and is 1 if they are equal."

Note that you could have learned much of that by browsing on http://mathworld.wolfram.com/
 
  • #4
Orthogonal is essentially a generalisation of the word perpendicular.
Orthonormal means both orthogonal and normalized.
 
  • #5
unfortunately there is also one standard misuse of the term orthogonal in the case of "orthogonal group" O(n), referring to the group of n by n matrices whose columns form an orthonormal basis.
 

FAQ: Orthonormal, Orthogonal, Perpendicular

What is the difference between orthonormal, orthogonal, and perpendicular?

Orthonormal, orthogonal, and perpendicular are often used interchangeably, but they have different meanings. Orthonormal refers to a set of vectors or functions that are both orthogonal and normalized. Orthogonal means that two vectors are perpendicular to each other, or at a right angle. Perpendicular specifically refers to two lines or vectors that intersect at a 90 degree angle.

How are orthonormal vectors useful in mathematics and science?

Orthonormal vectors are useful in many areas of mathematics and science, particularly in linear algebra, signal processing, and quantum mechanics. In linear algebra, orthonormal vectors can be used as a basis for vector spaces, making calculations and proofs simpler. In signal processing, they can be used to decompose signals into simpler components. In quantum mechanics, they are used to describe the state of a quantum system.

What does it mean for a matrix to be orthonormal?

A matrix is orthonormal if its columns (or rows) form an orthonormal set of vectors. This means that the columns (or rows) are both orthogonal and normalized. In other words, the dot product of any two columns (or rows) is 0, and the length of each column (or row) is 1. Orthonormal matrices are important in linear transformations and can be used to preserve the length and angle between vectors.

Can a set of vectors be orthonormal in one basis but not in another?

Yes, a set of vectors can be orthonormal in one basis but not in another. This is because the concept of orthogonality depends on the inner product used. Different inner products can lead to different definitions of orthogonality. Therefore, a set of vectors that may be orthogonal in one basis may not be orthogonal in another basis with a different inner product.

How can I determine if a set of vectors is orthonormal?

To determine if a set of vectors is orthonormal, you can use the following steps:

  1. Calculate the dot product of each pair of vectors. If the dot product is 0, the vectors are orthogonal.
  2. Calculate the length of each vector. If the length is 1, the vectors are normalized.
  3. Check that all vectors in the set are both orthogonal and normalized. If so, the set is orthonormal.

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