Knowen.md

Dashboard

Featured nodes

Roots

  • Public root

Templates

  • Guanyu's Latex template
  • Router template

Trees

  • Public trees
Related nodes

Parents1

  • Linear transformations

Siblings39
  • Sort by title
  • Sort by date

  • 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
Knowen.mdBeta
#
  • Explore
  • Sign in
  1. A linear transformation is diagonalizable if there is a basis such that each element is an eigenvector of the transformation.

Navigate

Jump to a node in this tree, or explore your trees and public knowledge.

Quick Navigation

Jump to a node or explore another tree.

  • Current tree nodes
  • Public trees
26–50 of 715 nodes
  • 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
« First ← Previous 1 2 3 4 5 6 ... Next → Last »
1–2 of 2 public trees
  • Aspen Summer 2016 - Entanglement Matters#1213
  • Math#23168

Knowen© 2026 KTree Inc.

ExplorePricingIntegrationsContact usLeave feedback