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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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  • The dimension of a direct sum of subspaces is the sum of the dimensions of the subspaces.#23971
  • The dimension of a eigenspace is less than or equal to the (algebraic) multiplicity of the eigenvalue.#23708
  • The dimension of a subspace is less than or equal to the dimension of the whole space#24010
  • The dimension of the domain of an injective linear transformation is at most the dimension of the codomain.#24033
  • The dimension of the domain of a surjective linear transformation is at least the dimension of the codomain.#24034
  • The direct sum of a subspace and its orthogonal complement is the whole space.#23922
  • The echelon form can be used to determine if a linear system is consistent.#23771
  • The eigenspace of a linear transformation is a nontrivial subspace.#24051
  • The eigenvalues of a matrix are the roots/solutions of its characteristic polynomial/equation.#23705
  • The eigenvalues of a polynomial of a matrix are the polynomial of the eigenvalues.#23691
  • The eigenvalues of a power of a matrix are the power the eigenvalues.#23690
  • The eigenvalues of a scalar multiple of a matrix are the scalar multiples of the eigenvalues.#23689
  • The eigenvalues of a triangular matrix are the entries on the main diagonal.#23709
  • The eigenvalues of the inverse of a nonsingular matrix are the reciprocals of the eigenvalues.#23692
  • The geometry of linear systems#23427
  • The Gram-Schmidt process converts a linearly independent set into an orthogonal set.#23936
  • The identity matrix is the identity for matrix multiplication.#23498
  • The image of a linearly independent set under an injective linear transformation is linearly independent.#24032
  • The image of a spanning set spans the range#24037
  • The inner product of a vector with itself is the square of its norm/length.#23910
  • The intersection of subspaces is a subspace#23967
  • The inverse image of a subspace under a linear transformation is a subspace.#24057
  • The inverse of a composition reverses the order#24030
  • The inverse of a linear transformation is a linear transformation#24028
  • The inverse of a matrix can be expressed in terms of its matrix of cofactors.#23721
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