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The concept of unit circle (the set of all vectors of norm 1) is different in different norms: for the 1-norm, the unit circle is a square oriented as a diamond; for the 2-norm (Euclidean norm), it is the well-known unit circle; while for the infinity norm, it is an axis-aligned square.
The Frobenius norm defined by ‖ ‖ = = = | | = = = {,} is self-dual, i.e., its dual norm is ‖ ‖ ′ = ‖ ‖.. The spectral norm, a special case of the induced norm when =, is defined by the maximum singular values of a matrix, that is, ‖ ‖ = (), has the nuclear norm as its dual norm, which is defined by ‖ ‖ ′ = (), for any matrix where () denote the singular values ...
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The Schatten 1-norm is the nuclear norm (also known as the trace norm, or the Ky Fan n-norm [1]). The Schatten 2-norm is the Frobenius norm. The Schatten ∞-norm is the spectral norm (also known as the operator norm, or the largest singular value).
[10] In a linear regression problem modeled as y ∼ G e n e r a l i z e d N o r m a l ( X ⋅ θ , α , p ) {\displaystyle y\sim \mathrm {GeneralizedNormal} (X\cdot \theta ,\alpha ,p)} , the MLE will be the arg min θ ‖ X ⋅ θ − y ‖ p {\displaystyle \arg \min _{\theta }\|X\cdot \theta -y\|_{p}} where the p-norm is used.
The Euclidean norm of a Euclidean vector space is a special case that allows defining Euclidean distance by the formula (,) = ‖ ‖. The study of normed spaces and Banach spaces is a fundamental part of functional analysis , a major subfield of mathematics.
Suppose a vector norm ‖ ‖ on and a vector norm ‖ ‖ on are given. Any matrix A induces a linear operator from to with respect to the standard basis, and one defines the corresponding induced norm or operator norm or subordinate norm on the space of all matrices as follows: ‖ ‖, = {‖ ‖: ‖ ‖ =} = {‖ ‖ ‖ ‖:} . where denotes the supremum.