Faster access than browser!Similarities between Eigendecomposition of a matrix and Orthogonal matrix
Eigendecomposition of a matrix and Orthogonal matrix have 19 things in common (in Unionpedia): Condition number, Determinant, Diagonal matrix, Diagonalizable matrix, Eigenvalues and eigenvectors, Gaussian elimination, Householder transformation, Invertible matrix, Linear algebra, Matrix decomposition, Newton's method, Normal matrix, Numerical analysis, Orthogonality, Singular-value decomposition, Spectral theorem, Symmetric matrix, Unit vector, Unitary matrix.
Condition number
In the field of numerical analysis, the condition number of a function with respect to an argument measures how much the output value of the function can change for a small change in the input argument.
Condition number and Eigendecomposition of a matrix · Condition number and Orthogonal matrix ·
Determinant
In linear algebra, the determinant is a value that can be computed from the elements of a square matrix.
Determinant and Eigendecomposition of a matrix · Determinant and Orthogonal matrix ·
Diagonal matrix
In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero.
Diagonal matrix and Eigendecomposition of a matrix · Diagonal matrix and Orthogonal matrix ·
Diagonalizable matrix
In linear algebra, a square matrix A is called diagonalizable if it is similar to a diagonal matrix, i.e., if there exists an invertible matrix P such that P−1AP is a diagonal matrix.
Diagonalizable matrix and Eigendecomposition of a matrix · Diagonalizable matrix and Orthogonal matrix ·
Eigenvalues and eigenvectors
In linear algebra, an eigenvector or characteristic vector of a linear transformation is a non-zero vector that changes by only a scalar factor when that linear transformation is applied to it.
Eigendecomposition of a matrix and Eigenvalues and eigenvectors · Eigenvalues and eigenvectors and Orthogonal matrix ·
Gaussian elimination
In linear algebra, Gaussian elimination (also known as row reduction) is an algorithm for solving systems of linear equations.
Eigendecomposition of a matrix and Gaussian elimination · Gaussian elimination and Orthogonal matrix ·
Householder transformation
In linear algebra, a Householder transformation (also known as a Householder reflection or elementary reflector) is a linear transformation that describes a reflection about a plane or hyperplane containing the origin.
Eigendecomposition of a matrix and Householder transformation · Householder transformation and Orthogonal matrix ·
Invertible matrix
In linear algebra, an n-by-n square matrix A is called invertible (also nonsingular or nondegenerate) if there exists an n-by-n square matrix B such that where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication.
Eigendecomposition of a matrix and Invertible matrix · Invertible matrix and Orthogonal matrix ·
Linear algebra
Linear algebra is the branch of mathematics concerning linear equations such as linear functions such as and their representations through matrices and vector spaces.
Eigendecomposition of a matrix and Linear algebra · Linear algebra and Orthogonal matrix ·
Matrix decomposition
In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices.
Eigendecomposition of a matrix and Matrix decomposition · Matrix decomposition and Orthogonal matrix ·
Newton's method
In numerical analysis, Newton's method (also known as the Newton–Raphson method), named after Isaac Newton and Joseph Raphson, is a method for finding successively better approximations to the roots (or zeroes) of a real-valued function.
Eigendecomposition of a matrix and Newton's method · Newton's method and Orthogonal matrix ·
Normal matrix
In mathematics, a complex square matrix is normal if where is the conjugate transpose of.
Eigendecomposition of a matrix and Normal matrix · Normal matrix and Orthogonal matrix ·
Numerical analysis
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics).
Eigendecomposition of a matrix and Numerical analysis · Numerical analysis and Orthogonal matrix ·
Orthogonality
In mathematics, orthogonality is the generalization of the notion of perpendicularity to the linear algebra of bilinear forms.
Eigendecomposition of a matrix and Orthogonality · Orthogonal matrix and Orthogonality ·
Singular-value decomposition
In linear algebra, the singular-value decomposition (SVD) is a factorization of a real or complex matrix.
Eigendecomposition of a matrix and Singular-value decomposition · Orthogonal matrix and Singular-value decomposition ·
Spectral theorem
In mathematics, particularly linear algebra and functional analysis, a spectral theorem is a result about when a linear operator or matrix can be diagonalized (that is, represented as a diagonal matrix in some basis).
Eigendecomposition of a matrix and Spectral theorem · Orthogonal matrix and Spectral theorem ·
Symmetric matrix
In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose.
Eigendecomposition of a matrix and Symmetric matrix · Orthogonal matrix and Symmetric matrix ·
Unit vector
In mathematics, a unit vector in a normed vector space is a vector (often a spatial vector) of length 1.
Eigendecomposition of a matrix and Unit vector · Orthogonal matrix and Unit vector ·
Unitary matrix
In mathematics, a complex square matrix is unitary if its conjugate transpose is also its inverse—that is, if where is the identity matrix.
Eigendecomposition of a matrix and Unitary matrix · Orthogonal matrix and Unitary matrix ·
The list above answers the following questions
- What Eigendecomposition of a matrix and Orthogonal matrix have in common
- What are the similarities between Eigendecomposition of a matrix and Orthogonal matrix
Eigendecomposition of a matrix and Orthogonal matrix Comparison
Eigendecomposition of a matrix has 69 relations, while Orthogonal matrix has 105. As they have in common 19, the Jaccard index is 10.92% = 19 / (69 + 105).
References
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