2. 3-Dimensional matrix norms
Matrix norms are essential parts of numerical linear algebra (see [12]) and its applications in science, engineering and
finance.
Definition. A 3-dimensional matrix norm ∥ · ∥ is a function from m-by-n-by-s complex matrices into R that satisfies the
following properties:
• ∥A∥ ≥ 0 and ∥A∥ = 0 if and only if A = 0;
• ∥αA∥ = |α| ∥A∥, for scalar α;
• ∥A + B∥ ≤ ∥A∥ + ∥B∥; where A and B are matrices in m-by-n-by-s dimensional space.
Definition. The 1-norm and∞-norm of A ∈ Cm×n×s are defined as follows:
∥A∥1 = max
1≤j≤n
s
k=1
m
i=1
|a(k)
ij | = the largest absolute block-column sum.
∥A∥∞ = max
1≤i≤m
s
k=1
n
j=1
|a(k)
ij | = the largest absolute block-row sum.
Lemma. Let A ∈ Cm×n×s then the ∥A∥1 and ∥A∥∞ are norms.
Proof. Proofs are straightforward and just come from the definition of them.
Definition. The p-norm of A ∈ Cm×n×s is defined as follows:
∥A∥p =
s
k=1
m
i=1
n
j=1
|a(k)
ij |p
1p
, for 1 < p < ∞.
Lemma. Let A be a matrix in m-by-n-by-s dimensional space then ∥A∥p is a norm.
Proof. • ∥A∥p ≥ 0 and ∥A∥p = 0 if and only if A = 0 (by the definition).
• ∥αA∥p = (
s
k=1
mi
=1
nj
=1 |αa(k)
ij |p)
1p
= (|α|ps
k=1
m
i=1
nj
=1 |a(k)
ij