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  • Linear transformations

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  • Eigenvalues and eigenvectors
  • The matrix representation of a sum of linear transformations is the sum of the matrices.
  • A linear transformation is invertible if and only if it is injective and surjective
  • Subspaces associated to a linear transformation
  • A linear transformation is diagonalizable if there is a basis such that each element is an eigenvector of the transformation.
  • The preimage of a vector is a translation of the kernel of the linear transformation
  • The matrix representation of a composition of linear transformations is the product of the matrices.
  • Geometric properties of linear transformations
  • Matrices as linear transformations
  • Basic properties of linear transformations
  • Description of a spanning set for the null space of a matrix from the reduced row-echelon form.
  • Description of a basis for the null space of a matrix from the reduced row-echelon form.
  • The nonzero rows of an echelon form of a matrix are linearly independent.
  • Subspaces associated to a matrix
  • Rank and nullity
  • Examples
  • Terminology
  • Composition
  • The image of a linearly independent set under an injective linear transformation is linearly independent.
  • The dimension of the domain of an injective linear transformation is at most the dimension of the codomain.
  • The dimension of the domain of a surjective linear transformation is at least the dimension of the codomain.
  • A linear transformation is surjective if and only if the rank equals the dimension of the codomain.
  • The range of a linear transformation is a subspace
  • The image of a spanning set spans the range
  • A linear transformation is surjective if and only if the image of a basis is a spanning set
  • Definition of generalized range space of a linear transformation
  • A linear transformation is injective on its generalized range space.
  • Definition of diagonalizable linear transformation
  • The rank plus the nullity of a linear transformation equals the dimension of the domain.
  • Linear maps preserve dependence of lists, with image-set caveats
  • A linear transformation is onto if and only if its rank equals the number of rows in any matrix representation.
  • Definition of matrix representation of a linear transformation with respect to bases of the spaces
  • A linear transformation is given by multiplying by its matrix representation with respect to bases of the spaces
  • Definition of matrix representation of a linear transformation from a vector space to itself
  • The matrix representation of a scalar multiple of linear transformations is the scalar multiple of the matrix.
  • The matrix of an inverse is the inverse of the matrix representation
  • Eigenvalues agree and eigenvectors correspond through coordinates
  • Triangularization requires the characteristic polynomial to split
  • Equivalence theorems for injective transformations
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  1. A linear transformation is diagonalizable if there is a basis such that each element is an eigenvector of the transformation.

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  • 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
  • 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 real or complex linear system has zero, one, or infinitely many solutions#23439
  • 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 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
  • A set of vectors is linearly independent if and only if the matrix of column vectors in reduced row-echelon form has every column as a pivot column.#23824
  • A square real matrix has orthonormal columns iff its inverse is its transpose#23930
  • A subset of a linearly independent set is linearly independent.#23980
  • A vector can be written uniquely as a linear combination of vectors from independent subspaces.#23973
  • A vector can be written uniquely as a sum of a vector in a subspace and a vector orthogonal to the subspace.#23934
  • A vector is in the orthogonal complement of a subspace if and only if it is orthogonal to every vector in a basis of the subspace.#23923
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  • Aspen Summer 2016 - Entanglement Matters#1213
  • Container for Linear Algebra#24123
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