Matrices
Addition 
X + Y = [zij] = [xij + yij] 
Subtraction 
X  Y = [zij] = [xij  yij] 
Multiplication 
X * Y = [zij] = [ xi * yj] 
Constant 
c * X = [zij] = [c * xij] 
Transpose & Identity
Transpose 
X^{T} = [zij] = [xji] 
Tr of Tr 
(X^{T})^{T} = X 
Tr of Mul 
(XY)^{T} = Y^{T} X^{T} != X^{T} Y^{T} 
Sym Matrix 
X^{T} = X 
Identity Matrix I [zii=1, zij=0] 
X I = I X = X 
Inverse
Inverse 
X X^{1} = I = X^{1}X 
if X^{1} exists then X is non singular or invertible 
Inv of Inv 
(X^{1})^{1} = X 
Inv of Mul 
(XY)^{1} = Y^{1}X^{1} != X^{1}Y^{1} 
Inv of Tr 
(X^{T})^{1} = (X^{1})^{T} 
Determinant 
A = ^{n}∑ i=1
a i
j
x Det a i
j
 
Determinant is computed over first row of matrix where each element of first row is multiplied by its minor 
minor M i
j
is a determinant obtained by deleting the i ^{th} row and j ^{th} column in which a i
j
lies. Minor of a i
j
is denoted by m i
j
. 
Cofactor 

Adjoint 
adj(A) = (Cofactor) ^{T} = (A i
j
) ^{T} 
Inverse 
A^{1} = adj(A) / A 


Orthogonal
Two n x 1 vectors are orthogonal if X^{T} Y = 0 
A vector is orthonormal if X^{T}X = X^{2} 
Sq root of X is length or norm of vector 
{X 1
, X 2
, X 3
.... X n
) are said to be orthonormal if, each pair is orthogonal and have unit length 
A sq matrix is orthogonal if X^{T}X= I or X^{T}=X^{1} 
Eigen Values & Eigen Vectors
A is nxn matrix, X is nx1 matrix, λ is a scalar, then 
AX = λX or (AλI)X = 0 or X = (AλI)^{1} 
λ is the eigen value and X is the eigen vector (non zero) 
Since X is non zero, AλI should be 0 
Determinant for [a b] = ad  bc [c d] 
If A => symmetric, then eigenvalues => real & eigenvectors => orthogonal 
Diagonalization: P => orthogonal matrix, then Z = P^{T}AP, Z is diagonal matrix with eigen values of A 
Linear Independence
Given a 1
x 1
+ a 2
x 2
+ ...a n
x n
= 0, if a vector [a 1
, a 2
, ...a n
] exists such that 
a. all a i
are 0, then x i
are linearly independent. 
b. if some a i
!=0 then x i
are linearly dependent. 
If a set of vectors are linearly dependent, then one of them can be written as some combination of others 
A set of two vectors is linearly dependent if and only if one of the vectors is a constant multiple of the other. 
Idempotence
a nxn matrix A is idempotent iff A^{2} = A 
The identity matrix I is idempotent. 
Let X be an n×k matrix of full rank ,n≥k then H exists as H=X(X^{T}X)^{−1}X^{T} and is idempotent. 


Rank
For a nxk matrix say X, the column vectors are [x 1
, x 2
, ...x k
] and rank is given by max num of linearly independent vectors. 
If X is a nxk matrix and r(X) = k, then X is of full rank for n≥k. 
r(X) = r(X^{T}) = r(X^{T}X) 
If X is kxk, then X is non singular iff r(X) = k. 
If X is n×k, P is n×n and nonsingular, and Q is k×k and nonsingular, then r(X) =r(PX) =r(XQ). 
The rank of a diagonal matrix is equal to the number of non zero diagonal entries in the matrix. 
r(XY) ≤ r(X) r(Y) 
Trace
The trace of a square k×k matrix X is sum of its diagonal entries  tr(X) = ∑ xii 
If c is a scalar, tr(cX) =c * tr(X) 
tr(X±Y) =tr(X) ± tr(Y). 
If XY and YX both exist, tr(XY) =tr(YX). 
Quadratic Forms
A be a k × k, y be k × 1 vector containing variables q = y^{T} Ay is called a quadratic form in y, A is called the matrix of the quadratic form 

If y^{T}Ay > 0 for all y != 0, y^{T}Ay & A are +ve definite 
If y^{T}Ay >= 0 for all y != 0, y^{T}Ay & A are +ve semidefinite 
Matrix Differentiation
y = (y1, y2, . . . , yk) ^{T}, z = f(y) then ∂z/∂y = [∂z/∂y 1
∂z/∂y 2
∂z/∂y 3
] ^{T} 
z=a^{T}y, ∂z/∂y = a 
z=y^{T}y, ∂z/∂y = 2y 
z=y^{T}Ay, ∂z/∂y = Ay+A^{T}y , if A is symmetrix then ∂z/∂y = 2Ay 
Theorems
Theorem 1 Let A be a symmetric k×k matrix. Then an orthogonal matrix P exists such that P ^{T}AP = λ x I, where λ = [λ 1
, λ 2
, .... λ n
] are the eigen values of A as nx1 vector 
Theorem 2 The eigenvalues of idempotent matrices are always either 0 or 1. 
Theorem 3 If A is a symmetric and idempotent matrix, r(A) =tr(A) 
Theorem 4 Let A 1
,A 2
,...,A m
be a collection of symmetric k×k matrices. Then the following are equivalent: a. There exists an orthogonal matrix P such that P ^{T}A i
P is diagonal for all i= 1,2,...,m; b. A i
A j
=A j
A i
for every pair i,j= 1,2,...,m. 
Theorem 5 Let A 1
,A 2
,...,A m
be a collection of symmetric k×k matrices. Then any two of the following conditions implies the third: a. All Ai, i= 1,2,...,m are idempotent; b. ∑ Ai is idempotent; c. AiAj= 0for i6=j 
Theorem 6 Let A 1
,A 2
,...,A m
be a collection of symmetric k×k matrices. If the conditions in Theorem 5 are true, then r(∑A i
) = ∑r(A i
) 
Theroem 7 A symmetric matrix A is positive definite if and only if its eigen values are all (strictly) positive 
Theorem 8 A symmetric matrix A is positive semidefinite if and only if its eigenvalues are all nonnegative. 

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