Science Fair Project Encyclopedia
In mathematics, the term matrix norm can have two meanings:
- A sub-multiplicative vector norm is any vector norm on square matrices compatible with matrix multiplication in the sense that
In the rest of the article, we will follow the tradition in matrix theory. We use term "vector norm" for the first definition and "matrix norm" for the second definition.
Equivalence of norms
For any two vector norms | · |1 and | · |2, we have
for some positive numbers r and s, for all matrices A. In order words, they are equivalent norms; they induce the same topology on the real or complex vector space.
Moreover, when m = n, then for any vector norm | · |, there exists a unique positive number k such that k| · | is a (submultiplicative) matrix norm.
A matrix norm || · || is said to be minimal if there exists no other matrix norm | · | satisfying |A|≤||A|| for all |A|.
Operator norm or induced norm
If m = n and one uses the same norm on domain and range, then these operator norms are all (submultiplicative) matrix norms.
Spectral norm or spectral radius
If m=n and the norm on Kn is the Euclidean norm, then the induced matrix norm is the spectral norm.
An important property for matrix norm is
where ρ(A) is the spectral radius of A.
These vector norms treat a matrix as an vector, and use one of the familiar vector norms. For example, for k=1,2,..., we have the following k-norm:
For k=2, it corresponds to the Euclidean norm and is called the Frobenius norm. Most entrywise norms are not (submultiplicative) matrix norms.
The Frobenius norm of A is defined as
where A* denotes the conjugate transpose of A, σi are the singular values of A, and the trace function is used. This norm is very similar to the Euclidean norm on Kn and comes from an inner product on the space of all matrices.
Frobenius norm is submultiplicative and is very useful numerical linear algebra. This norm is often more natural and more convenient than the induced norms.
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