Matrix Product

$ M_1=[a_{ij}] $ is a matrix of $ m $ lines and $ n $ columns and $ M_2=[b_{ij}] $ is a matrix of $ n $ lines and $ p $ columns (2x2,2x3,3x2,3x3,etc.). The matrix product $ M_1.M_2 = [c_{ij}] $ is a matrix of $ m $ lines and $ p $ columns, with: $$ \forall i, j : c_{ij} = \sum_{k=1}^n a_{ik}b_{kj} $$

Example: $$ \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} \cdot \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = \begin{bmatrix} 1 \times 1 + 2 \times 0 & 1 \times 0 + 2 \times 1 \\ 3 \times 1 + 4 \times 0 & 3 \times 0 + 4 \times 1 \end{bmatrix} = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} $$

The order of the operands matters with matrix computations, so $$ M_1.M_2 \neq M_2.M_1 $$

The product of the matrix $ M=[a_{ij}] $ by a scalar (number) $ \lambda $ is a matrix of the same size than the initial matrix $ M $, with each items of the matrix multiplied by $ \lambda $.

$$ \lambda M = [ \lambda a_{ij} ] $$

Associativity : $$ A \times (B \times C) = (A \times B) \times C $$

Distributivity : $$ A \times (B + C) = A \times B + A \times C $$

$$ (A + B) \times C = A \times C + B \times C $$

$$ \lambda (A \times B) = (\lambda A) \times B = A \times (\lambda B) $$

There is a matrix product compatible with any matrix sizes: the Kronecker product.