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  • Equivalence theorems for injective transformations

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  • Definition of one-to-one/injective linear transformation
  • Equivalence theorem for injective linear transformations: The inverse of T is a linear transformation on its range.
  • Equivalence theorem for injective linear transformations: The null space of T is 0.
  • Equivalence theorem for injective linear transformations: The nullity of T is 0.
  • Equivalence theorem for injective linear transformations: The kernel of T is 0.
  • Equivalence theorem for injective linear transformations: T(x)=0 only for x=0.
  • Equivalence theorem for injective linear transformations: The rank of T is n.
  • Equivalence theorem for injective linear transformations: The rank of T is equals the number of columns in any matrix representation..
  • Equivalence theorem for injective linear transformations: The image of a basis for V is a basis for the range of T.
  • Equivalence theorem for injective linear transformations: The columns of the matrix of T are linearly independent.
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  1. Equivalence theorem for injective linear transformations: The rank of T is equals the number of columns in any matrix representation..

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  • An n-by-n matrix is diagonalizable if and only if the union of the basis vectors for the eigenspaces is a basis for R^n (or C^n).#23616
  • An n-by-n matrix nas n (complex) eigenvalues#23698
  • An n-by-n matrix with n distinct eigenvalues is diagonalizable.#23617
  • A nonempty subset of a vector space is a subspace if and only if it is closed under linear combinations#23965
  • A nonsingular matrix can be written as a product of elementary matrices.#23558
  • An orthogonal set of nonzero vectors is linearly independent.#23927
  • Any linearly independent set can be expanded to a basis for the (sub)space#23984
  • Any matrix times the 0 matrix equals the 0 matrix.#23491
  • Any vector space is the direct sum of the generalized kernel and gneralized range of a linear transformation on that space.#24061
  • Application Leontief input-output analysis#24118
  • Applications#24113
  • Applications of band matrices#24116
  • Applications to cubic spline#24120
  • Applications to differential equations#24114
  • Applications to error-correcting code#24117
  • Applications to Markov chains#24115
  • Applications to voting and social choice#24119
  • A scalar multiple of a linear transformation is a linear transformation#24018
  • A set is a basis if each vector can be written uniquely as a linear combination.#23988
  • A set is linearly independent if and only if the set of coordinate vectors with respect to any basis is linearly independent.#23981
  • A set of nonzero vectors contains (as a subset) a basis for its span.#23985
  • A set of two vectors is linearly dependent if and only if neither is a scalar multiple of the other.#23826
  • A set of vectors containing fewer elements than the dimension of the space cannot span#23979
  • A set of vectors containing more elements than the dimension of the space must be linearly dependent#24012
  • A set of vectors is linearly independent if and only if the homogeneous linear system corresponding to the matrix of column vectors has only the trivial solution.#23823
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  • Aspen Summer 2016 - Entanglement Matters#1213
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