In mathematics, a matrix norm is a natural extension of the notion of a vector norm to matrices.
Contents
Definition
In what follows,
A matrix norm is a vector norm on
Additionally, in the case of square matrices (thus, m = n), some (but not all) matrix norms satisfy the following condition, which is related to the fact that matrices are more than just vectors:
A matrix norm that satisfies this additional property is called a submultiplicative norm (in some books, the terminology matrix norm is used only for those norms which are submultiplicative). The set of all
The definition of submultiplicativity is sometimes extended to non-square matrices, for instance in the case of the induced p-norm, where for
Induced norm
If vector norms on Km and Kn are given (K is the field of real or complex numbers), then one defines the corresponding induced norm or operator norm on the space of m-by-n matrices as the following suprema:
The operator norm corresponding to the p-norm for vectors, p ≥ 1, is:
These are different from the entrywise p-norms, p ≥ 1, and the Schatten p-norms for matrices treated below, which are also usually denoted by
In the special cases of p = 1,2,∞, the norms can be computed or estimated by
For example, if the matrix A is defined by
then we have
and
In the special case of p = 2 (the Euclidean norm), the induced matrix norm is the spectral norm. The spectral norm of a matrix A is the largest singular value of A i.e. the square root of the largest eigenvalue of the positive-semidefinite matrix A∗A:
where A∗ denotes the conjugate transpose of A.
More generally, one can define the subordinate matrix norm on
Subordinate norms are consistent with the norms that induce them, giving
For
Any induced norm satisfies the inequality
where ρ(A) is the spectral radius of A. For a symmetric or hermitian matrix
the spectral radius of
Furthermore, for square matrices we have the spectral radius formula:
"Entrywise" norms
These vector norms treat an
For example, using the p-norm for vectors, p ≥ 1, we get:
This is a different norm from the induced p-norm (see above) and the Schatten p-norm (see below), but the notation is the same.
The special case p = 2 is the Frobenius norm, and p = ∞ yields the maximum norm.
L2,1 and Lp,q norms
Let
The
The
Frobenius norm
When p = q = 2 for the
where A∗ denotes the conjugate transpose of A, σi are the singular values of A, and the trace function is used. The Frobenius norm is the Euclidean norm on
The Frobenius norm is sub-multiplicative and is very useful for numerical linear algebra. This norm is often easier to compute than induced norms and has the useful property of being invariant under rotations, that is,
and
where we have used the orthogonal nature of
It also satisfies
and
where
Max norm
The max norm is the elementwise norm with p = q = ∞:
This norm is not sub-multiplicative.
Schatten norms
The Schatten p-norms arise when applying the p-norm to the vector of singular values of a matrix. If the singular values are denoted by σi, then the Schatten p-norm is defined by
These norms again share the notation with the induced and entrywise p-norms, but they are different.
All Schatten norms are sub-multiplicative. They are also unitarily invariant, which means that ||A|| = ||UAV|| for all matrices A and all unitary matrices U and V.
The most familiar cases are p = 1, 2, ∞. The case p = 2 yields the Frobenius norm, introduced before. The case p = ∞ yields the spectral norm, which is the matrix norm induced by the vector 2-norm (see above). Finally, p = 1 yields the nuclear norm (also known as the trace norm, or the Ky Fan 'n'-norm), defined as
(Here
Consistent norms
A matrix norm
for all
Compatible norms
A matrix norm
for all
Equivalence of norms
For any two matrix norms
for some positive numbers r and s, for all matrices A in
Moreover, for every vector norm
A sub-multiplicative matrix norm
Examples of norm equivalence
Let
For matrix
Another useful inequality between matrix norms is
which is a special case of Hölder's inequality.