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  • Linear transformations

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  • Eigenvalues and eigenvectors
  • Terminology
  • 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
  • Composition
  • The preimage of a vector is a translation of the kernel of the linear transformation
  • 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 the image of a spanning set is a spanning set for the range space
  • 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
  • A linear transformation is diagonalizable if there is a basis such that each element is an eigenvector of the transformation.
  • Subspaces associated to a linear transformation
  • The rank plus the nullity of a linear transformation equals the dimension of the domain.
  • The image of a linearly dependent set under a linear transformation is linearly dependent.
  • A linear transformation is onto if and only if its rank equals the number of rows in any matrix representation.
  • A linear transformation is invertible if and only if it is injective and surjective
  • 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 representation of a sum of linear transformations is the sum of the matrices.
  • The matrix representation of a composition of linear transformations is the product of the matrices.
  • The matrix representation of the inverse of linear transformations is the inverse of the matricix.
  • A linear transformation has the same eigenvalues and eigenvectors as any matrix representation.
  • A linear transformation has a representation as an upper triangular matrix.
  • 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 left null space of a matrix is a subspace of R^m (or C^m).#24111
  • The matrix equation Ax=b has a solution if and only if b is a linear combination of the columns of A.#23768
  • The matrix representation of a composition of linear transformations is the product of the matrices.#24073
  • The matrix representation of a scalar multiple of linear transformations is the scalar multiple of the matrix.#24071
  • The matrix representation of a sum of linear transformations is the sum of the matrices.#24072
  • The matrix representation of the inverse of linear transformations is the inverse of the matricix.#24074
  • The minimal polynomial of a linear transformation exists and is unique.#24047
  • The minimal polynomial of a square matrix exists and is unique.#23704
  • The nonzero rows of an echelon form of a matrix are linearly independent.#23871
  • The nonzero rows of the reduced row-echelon form of a matris are a basis for the row space.#23879
  • The null space of a matrix is a subspace of R^n (or C^n).#24110
  • The null space of a matrix is the orthogonal complement of the column space.#23924
  • The number of pivots in the reduced row echelon form of a consistent system determines the number of free variables in the solution set.#23774
  • The number of pivots in the reduced row echelon form of a consistent system determines whether there is one or infinitely many solutions.#23773
  • The number of solutions to a linear system#23438
  • Theorem: a set of vectors is linearly dependent if and only if one of the vectors can be written as a linear combination of the other vectors#23820
  • Theorem: a set of vectors is linearly independent if and only if whenever a linear combination is 0#23822
  • Theorem characterizing when a space is the direct sum of two subspaces#23976
  • Theorem describing matrix multiplication#23490
  • Theorem describing properties of the block matrices of the extended reduced row echelon form of a matrix#23551
  • Theorem describing spaces associated to the block matrices of the extended reduced row echelon form of a matrix#23552
  • Theorem describing the determinants of elementary matrices.#23745
  • Theorem describing the dimension of spaces associated to the block matrices of the extended reduced row echelon form of a matrix#23553
  • Theorem describing the vector form of sulutions to a linear system.#23444
  • The orthogonal complement of a subspace is a subspace.#23921
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