Adding corresponding elements adds matrices {matrix addition}. Adding vectors is an example. Summing matrices is like adding one effect to another effect to get total effect.
To multiply matrices {matrix multiplication} {matrix dot product}, multiply each row by each column. Matrix with m columns and n rows times matrix with n columns and p rows makes matrix of m columns and p rows. First-matrix rows and second-matrix columns must have same rank. For 1x1 matrices [a11] and [b11], matrix dot product is [a11*b11]. For 2x2 matrices [a11 a12 / a21 a22] and [b11 b12 / b21 b22], matrix dot product is [a11*b11 + a11*b21 a12*b12 + a12*b22 / a21*b11 + a21*b21 a22*b12 + a22*b22]. For example, [1 2 / 3 4] . [5 4 / 3 2] = [1*5+1*3 2*4+2*2 / 3*5+3*3 4*4+4*2].
vector
Vector dot products are matrix multiplications of one-row 1xN matrix with one-column Nx1 matrix.
properties
Matrix multiplication is not commutative but is associative.
purposes
Multiplying matrices indicates results of interactions between two effects. Squaring matrix is like repeating operation.
Cross products {matrix cross product} of two square matrices indicate interactions between set-A and set-B members: A x B. Matrix cross products can find extensive quantities, such as area, from intensive quantities, such as vector distances. Matrix cross products are differences between matrix dot product and reverse matrix dot product: A x B = (A . B) - (B . A). Only square matrices can have matrix cross products. Matrix cross products find square matrices. For 1x1 matrices [a11] and [b11], matrix cross product is [a11*b11 - b11*a11] = [0]. For 2x2 matrices [a11 a12 / a21 a22] and [b11 b12 / b21 b22], matrix cross product is [a11*b11 + a11*b21 a12*b12 + a12*b22 / a21*b11 + a21*b21 a22*b12 + a22*b22] - [b11*a11 + b11*a21 b12*a12 + b12*a22 / b21*a11 + b21*a21 b22*a12 + b22*a22] = [a11*b11 + a11*b21 - b11*a11 - b11*a21 a12*b12 + a12*b22 - b12*a12 - b12*a22 / a21*b11 + a21*b21 - b21*a11 - b21*a21 a22*b12 + a22*b22 - b22*a12 - b22*a22] = [a11*b21 - b11*a21 a12*b22 - b12*a22 / a21*b11 - b21*a11 a22*b12 - b22*a12]. If both matrices are the same, matrix cross product is zero matrix: A x A = 0. Matrix cross products are not commutative: A x B = (A . B) - (B . A) <> (B . A) - (A . B) = B x A.
If M is a square matrix and another matrix is equivalent to M, their difference is zero matrix {Cayley-Hamilton theorem}. Theorem helps find characteristic equation.
For non-unitary matrices, replacing each matrix element by its complex conjugate and transposing the matrix {Hermitean operation, transposing} makes the same matrix {conjugate transpose}. For unitary matrices, matrix conjugate transpose is matrix inverse.
Interchanging any two matrix rows does not change matrix meaning {equivalence operation}. Multiplying all elements in row by non-zero number does not change matrix meaning. Replacing row by sum of itself and another row does not change matrix meaning.
Replacing each matrix element by its complex conjugate and transposing matrix {Hermitean operation, matrix} makes a matrix. Hermitean operators follow general eigenvalue theory, where (R(f),g) = (f,R(g)) and R is linear.
Square-matrix diagonal elements have a sum {trace, sum}. Matrix-product trace is first-matrix trace times second-matrix trace. Finding matrix-product traces is commutative: trace(A . B) = trace(B . A).
3-Algebra-Equation-System-Matrix
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Date Modified: 2022.0225