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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.
  • For matrices
  • 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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  • A linear transformation is surjective if and only if the rank equals the dimension of the codomain.#24035
  • A linear transformation maps 0 to 0.#23864
  • A linear transformation of a linear combination is the linear combination of the linear transformation#23865
  • A linear transformation on a finite dimentional nontrivial vector space has at least one eigenvalue.#24049
  • All echelon forms of a linear system have the same free variables#23423
  • A matrix and its transpose have the same determinant.#23728
  • A matrix and its transpose have the same eigenvalues/characteristic polynomial.#23693
  • A matrix A with real entries has orthonormal columns if and only if A inverse equals A transpose.#23930
  • A matrix equation is equivalent to a linear system#23767
  • A matrix is called ill-conditioned if it is nearly singular#23749
  • A matrix is nilpotent if and only if its only eigenvalue is 0.#23583
  • A matrix is orthogonally diagonalizable if and only if it is normal (The principal axis theorem).#23601
  • A matrix is orthogonally diagonalizable if and only if it is symmetric.#23578
  • A matrix of rank k is equivalent to a matrix with 1 in the first k diagonal entries and 0 elsewhere.#23606
  • A matrix turns into its adjoint when moved to the other side of the standard inner product on C^n.#23906
  • A matrix with a 0 row/column has determinant 0#23741
  • A matrix with real entries and orthonormal columns preserves dot products.#23932
  • A matrix with real entries and orthonormal columns preserves norms.#23931
  • A matrix with real entries has eigenvalues occurring in conjugate pairs.#23710
  • A matrix with two equal rows/columns has determinant 0#23740
  • An eigenspace of a matrix is a nontrivial subspace.#23685
  • An eigenspace of a matrix is the null space of a related matrix.#23686
  • An n-by-n matrix is diagonalizable if and only if it has n linearly independent eigenvectors.#23612
  • An n-by-n matrix is diagonalizable if and only if the characteristic polynomial factors completely#23614
  • An n-by-n matrix is diagonalizable if and only if the sum of the dimensions of the eigenspaces equals n.#23613
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  • Aspen Summer 2016 - Entanglement Matters#1213
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