From Wikipedia, the free encyclopedia
Illustration of the addition of two matrices.
Notions of sums for matrices in linear algebra
In mathematics , matrix addition is the operation of adding two matrices by adding the corresponding entries together.
For a vector ,
v
→
{\displaystyle {\vec {v}}\!}
, adding two matrices would have the geometric effect of applying each matrix transformation separately onto
v
→
{\displaystyle {\vec {v}}\!}
, then adding the transformed vectors.
A
v
→
+
B
v
→
=
(
A
+
B
)
v
→
{\displaystyle \mathbf {A} {\vec {v}}+\mathbf {B} {\vec {v}}=(\mathbf {A} +\mathbf {B} ){\vec {v}}\!}
Two matrices must have an equal number of rows and columns to be added.[ 1] In which case, the sum of two matrices A and B will be a matrix which has the same number of rows and columns as A and B . The sum of A and B , denoted A + B , is computed by adding corresponding elements of A and B :
A
+
B
=
[
a
11
a
12
⋯
a
1
n
a
21
a
22
⋯
a
2
n
⋮
⋮
⋱
⋮
a
m
1
a
m
2
⋯
a
m
n
]
+
[
b
11
b
12
⋯
b
1
n
b
21
b
22
⋯
b
2
n
⋮
⋮
⋱
⋮
b
m
1
b
m
2
⋯
b
m
n
]
=
[
a
11
+
b
11
a
12
+
b
12
⋯
a
1
n
+
b
1
n
a
21
+
b
21
a
22
+
b
22
⋯
a
2
n
+
b
2
n
⋮
⋮
⋱
⋮
a
m
1
+
b
m
1
a
m
2
+
b
m
2
⋯
a
m
n
+
b
m
n
]
{\displaystyle {\begin{aligned}\mathbf {A} +\mathbf {B} &={\begin{bmatrix}a_{11}&a_{12}&\cdots &a_{1n}\\a_{21}&a_{22}&\cdots &a_{2n}\\\vdots &\vdots &\ddots &\vdots \\a_{m1}&a_{m2}&\cdots &a_{mn}\\\end{bmatrix}}+{\begin{bmatrix}b_{11}&b_{12}&\cdots &b_{1n}\\b_{21}&b_{22}&\cdots &b_{2n}\\\vdots &\vdots &\ddots &\vdots \\b_{m1}&b_{m2}&\cdots &b_{mn}\\\end{bmatrix}}\\&={\begin{bmatrix}a_{11}+b_{11}&a_{12}+b_{12}&\cdots &a_{1n}+b_{1n}\\a_{21}+b_{21}&a_{22}+b_{22}&\cdots &a_{2n}+b_{2n}\\\vdots &\vdots &\ddots &\vdots \\a_{m1}+b_{m1}&a_{m2}+b_{m2}&\cdots &a_{mn}+b_{mn}\\\end{bmatrix}}\\\end{aligned}}\,\!}
Or more concisely (assuming that A + B = C ):[ 4]
c
i
j
=
a
i
j
+
b
i
j
{\displaystyle c_{ij}=a_{ij}+b_{ij}}
For example:
[
1
3
1
0
1
2
]
+
[
0
0
7
5
2
1
]
=
[
1
+
0
3
+
0
1
+
7
0
+
5
1
+
2
2
+
1
]
=
[
1
3
8
5
3
3
]
{\displaystyle {\begin{bmatrix}1&3\\1&0\\1&2\end{bmatrix}}+{\begin{bmatrix}0&0\\7&5\\2&1\end{bmatrix}}={\begin{bmatrix}1+0&3+0\\1+7&0+5\\1+2&2+1\end{bmatrix}}={\begin{bmatrix}1&3\\8&5\\3&3\end{bmatrix}}}
Similarly, it is also possible to subtract one matrix from another, as long as they have the same dimensions. The difference of A and B , denoted A − B , is computed by subtracting elements of B from corresponding elements of A , and has the same dimensions as A and B . For example:
[
1
3
1
0
1
2
]
−
[
0
0
7
5
2
1
]
=
[
1
−
0
3
−
0
1
−
7
0
−
5
1
−
2
2
−
1
]
=
[
1
3
−
6
−
5
−
1
1
]
{\displaystyle {\begin{bmatrix}1&3\\1&0\\1&2\end{bmatrix}}-{\begin{bmatrix}0&0\\7&5\\2&1\end{bmatrix}}={\begin{bmatrix}1-0&3-0\\1-7&0-5\\1-2&2-1\end{bmatrix}}={\begin{bmatrix}1&3\\-6&-5\\-1&1\end{bmatrix}}}
^ Elementary Linear Algebra by Rorres Anton 10e p53
^ Weisstein, Eric W. "Matrix Addition" . mathworld.wolfram.com . Retrieved 2020-09-07 .
Linear equations Matrices
Matrix
Matrix addition
Matrix multiplication
Basis transformation matrix
Characteristic polynomial
Spectrum
Trace
Eigenvalue, eigenvector and eigenspace
Cayley–Hamilton theorem
Jordan normal form
Weyr canonical form
Rank
Inverse , Pseudoinverse
Adjugate , Transpose
Dot product
Symmetric matrix , Skew-symmetric matrix
Orthogonal matrix , Unitary matrix
Hermitian matrix , Antihermitian matrix
Positive-(semi)definite
Pfaffian
Projection
Spectral theorem
Perron–Frobenius theorem
Diagonal matrix , Triangular matrix , Tridiagonal matrix
Block matrix
Sparse matrix
Hessenberg matrix , Hessian matrix
Vandermonde matrix
Stochastic matrix , Toeplitz matrix , Circulant matrix , Hankel matrix
(0,1)-matrix
List of matrices
Matrix decompositions Relations and computations Vector spaces Structures Multilinear algebra Affine and projective Numerical linear algebra