Perform element-wise addition and subtraction on matrices of any dimension. Essential linear algebra tool for students and engineers.
Matrix Addition Formula: C = A + B where cᵢⱼ = aᵢⱼ + bᵢⱼ
Matrix Subtraction Formula: C = A - B where cᵢⱼ = aᵢⱼ - bᵢⱼ
Matrix addition and subtraction are fundamental operations in linear algebra. They are performed element-wise, meaning corresponding elements from each matrix are added or subtracted.
Matrix Addition Formula:
C = A + B where cᵢⱼ = aᵢⱼ + bᵢⱼ
For all i = 1,...,m and j = 1,...,n
Matrix Subtraction Formula:
C = A - B where cᵢⱼ = aᵢⱼ - bᵢⱼ
For all i = 1,...,m and j = 1,...,n
Same Dimensions: Both matrices must have the same number of rows and columns. For matrices A (m×n) and B (p×q), we require m = p and n = q.
Element-wise Operation: Each element in the result matrix is computed independently by adding or subtracting corresponding elements from the input matrices.
Result Dimension: The resulting matrix has the same dimensions as the input matrices (m×n).
| Property | Formula | Description |
|---|---|---|
| Commutative | A + B = B + A | Order doesn't matter for addition |
| Associative | (A + B) + C = A + (B + C) | Grouping doesn't matter for addition |
| Additive Identity | A + 0 = A | Zero matrix is the identity element |
| Additive Inverse | A + (-A) = 0 | Every matrix has an additive inverse |
| Distributive (Scalar) | k(A + B) = kA + kB | Scalar multiplication distributes over addition |
| Property | Formula | Description |
|---|---|---|
| Non-commutative | A - B ≠ B - A (in general) | Order matters for subtraction |
| Non-associative | (A - B) - C ≠ A - (B - C) | Grouping matters for subtraction |
| Relation to Addition | A - B = A + (-B) | Subtraction is addition of the additive inverse |
| Zero Result | A - A = 0 | A matrix minus itself gives the zero matrix |
Linear Transformations: Matrix addition represents the superposition of linear transformations.
Computer Graphics: Used in transformations, animations, and combining multiple transformations.
Data Analysis: Combining datasets, calculating differences between data matrices.
Physics and Engineering: Solving systems of equations, electrical circuit analysis, structural analysis.
Economics: Input-output analysis, portfolio optimization, economic modeling.
Calculator Features:
| Matrix Type | Definition | Example |
|---|---|---|
| Zero Matrix | All elements are 0 | $\begin{bmatrix}0&0\\0&0\end{bmatrix}$ |
| Identity Matrix | Square matrix with 1s on diagonal, 0s elsewhere | $\begin{bmatrix}1&0\\0&1\end{bmatrix}$ |
| Diagonal Matrix | Square matrix with non-zero elements only on diagonal | $\begin{bmatrix}a&0\\0&d\end{bmatrix}$ |
| Symmetric Matrix | Square matrix equal to its transpose | $\begin{bmatrix}a&b\\b&c\end{bmatrix}$ |
| Upper Triangular | Square matrix with zeros below the diagonal | $\begin{bmatrix}a&b\\0&d\end{bmatrix}$ |
| Lower Triangular | Square matrix with zeros above the diagonal | $\begin{bmatrix}a&0\\c&d\end{bmatrix}$ |
A = [aᵢⱼ]
Matrix with elements aᵢⱼ
m × n
Matrix with m rows, n columns
A + B
Matrix addition
A - B
Matrix subtraction
0
Zero matrix
I
Identity matrix