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  • Multiplication

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  • Definition of matrix multiplication in terms of column vectors
  • Definition of matrix multiplication
  • Theorem describing matrix multiplication
  • Any matrix times the 0 matrix equals the 0 matrix.
  • Example of multiplying 2x2 matrices
  • Example of multiplying 3x3 matrices
  • Example of multiplying nonsquare matrices
  • Example of multiplying matrices
  • Matrix multiplication is associative.
  • Matrix multiplication is distributive over matrix addition.
  • The identity matrix is the identity for matrix multiplication.
  • Matrix multiplication is not commutative in general.
  • Matrix cancellation requires an invertibility or rank hypothesis
  • Matrix multiplication can be viewed as the dot product of a row vector of column vectors with a column vector of row vectors
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  1. Matrix multiplication can be viewed as the dot product of a row vector of column vectors with a column vector of row vectors

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  • The complex inner product is conjugate symmetric#23898
  • The composition of injective linear transformations is injective#24025
  • The composition of invertible linear transformations is invertible#24027
  • The composition of linear transformations is a linear transformation#24024
  • The composition of surjective linear transformations is surjective#24026
  • The conjugate of a matrix-scalar product is the product of the conjugates.#23468
  • The conjugate of a product of matrices is the product of the conjugates.#23469
  • The conjugate of a sum of vectors in C^n is the sum of the conjugates#23791
  • The conjugate of the sum of matrices is the sum of the conjugates.#23467
  • The conjugate of the transpose is the transpose of the conjugate.#23505
  • The conjugate of vector-scalar multiplication in C^n is the product of the conjugates.#23793
  • The coordinate vector relative to a given basis is a linear mapping to R^n (or C^n).#23992
  • The coordinate vector relative to a given basis is an injective linear mapping to R^n (or C^n).#23993
  • The coordinate vector relative to a given basis is a surjective linear mapping to R^n (or C^n).#23994
  • The crazy vector space is a vector space.#24107
  • The determinant function exists.#23734
  • The determinant function is unique.#23735
  • The determinant of a block diagonal matrix is the product of the determinants of the blocks.#23731
  • The determinant of a matrix can be computed as a cofactor expansion across any row.#23719
  • The determinant of a matrix can be computed as a cofactor expansion down any column.#23720
  • The determinant of a matrix can be expressed as a product of the diagonal entries in a non-scaled echelon form.#23725
  • The determinant of a matrix measures the area/volume of the parallelogram/parallelipiped determined by its columns.#23746
  • The determinant of a triangular matrix is the product of the entries on the diagonal.#23744
  • The determinant of the inverse of A is the reciprocal of the determinant of A.#23730
  • The determinant of the matrix of a linear transformation is the factor by which the area/volume changes.#23747
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