Frequently Used Notation and Terminology
dim V , 3 dimension of vector space V
M
n
, 8 n ×n (i.e., n-square) matrices with complex entries
A = (a
ij
), 8 matrix A with (i, j)-entry a
ij
I, 9 identity matrix
A
T
, 9 transpose of matrix A
A, 9 conjugate of matrix A
A
∗
, 9 conjugate transpose of matrix A, i.e., A
∗
= A
T
A
−1
, 13 inverse of matrix A
rank (A), 11 rank of matrix A
tr A, 21 trace of matrix A
det A, 12 determinant of matrix A
|A|, 12, 83, 164 determinant for a block matrix A or (A
∗
A)
1/2
or (|a
ij
|)
(u, v), 27 inner product of vectors u and v
∥ · ∥, 28, 113 norm of a vector or a matrix
Ker(A), 17 kernel or null space of A, i.e., Ker(A) = {x : Ax = 0}
Im(A), 17 image space of A, i.e., Im(A) = {Ax}
ρ(A), 109 spectral radius of matrix A
σ
max
(A), 109 largest singular value (spectral norm) of matrix A
λ
max
(A), 124 largest eigenvalue of matrix A
A ≥ 0, 81 A is positive semidefinite (or all a
ij
≥ 0 in Section 5.7)
A ≥ B, 81 A −B is positive semidefinite (or a
ij
≥ b
ij
in Section 5.7)
A ◦ B, 117 Hadamard (entrywise) product of matrices A and B
A ⊗ B, 117 Kronecker (tensor) product of matrices A and B
x ≺
w
y, 326 weak majorization, i.e., all
∑
k
i=1
x
↓
i
≤
∑
k
i=1
y
↓
i
hold
x ≺
wlog
y, 344 weak log-majorization, i.e., all
∏
k
i=1
x
↓
i
≤
∏
k
i=1
y
↓
i
hold
An n × n matrix A is said to be
upper-triangular if all entries below the main diagonal are zero
diagonalizable if P
−1
AP is diagonal for some invertible matrix P
similar to B if P
−1
AP = B for some invertible matrix P
unitarily similar to B if U
∗
AU = B for some unitary matrix U
unitary if AA
∗
= A
∗
A = I, i.e., A
−1
= A
∗
positive semidefinite if x
∗
Ax ≥ 0 for all vectors x ∈ C
n
Hermitian if A = A
∗
normal if A
∗
A = AA
∗
λ ∈ C is an eigenvalue of A ∈ M
n
if Ax = λx for some nonzero x ∈ C
n
.
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