Matrices

Diagonalizable matrix

In linear algebra, a square matrix is called diagonalizable or non-defective if it is similar to a diagonal matrix, i.e., if there exists an invertible matrix and a diagonal matrix such that , or equivalently . (Such , are not unique.) For a finite-dimensional vector space , a linear map is called diagonalizable if there exists an ordered basis of consisting of eigenvectors of . These definitions are equivalent: if has a matrix representation as above, then the column vectors of form a basis consisting of eigenvectors of , and the diagonal entries of are the corresponding eigenvalues of ; with respect to this eigenvector basis, is represented by . Diagonalization is the process of finding the above and . Diagonalizable matrices and maps are especially easy for computations, once their eigenvalues and eigenvectors are known. One can raise a diagonal matrix to a power by simply raising the diagonal entries to that power, and the determinant of a diagonal matrix is simply the product of all diagonal entries; such computations generalize easily to . Geometrically, a diagonalizable matrix is an inhomogeneous dilation (or anisotropic scaling) — it scales the space, as does a homogeneous dilation, but by a different factor along each eigenvector axis, the factor given by the corresponding eigenvalue. A square matrix that is not diagonalizable is called defective. It can happen that a matrix with real entries is defective over the real numbers, meaning that is impossible for any invertible and diagonal with real entries, but it is possible with complex entries, so that is diagonalizable over the complex numbers. For example, this is the case for a generic rotation matrix. Many results for diagonalizable matrices hold only over an algebraically closed field (such as the complex numbers). In this case, diagonalizable matrices are dense in the space of all matrices, which means any defective matrix can be deformed into a diagonalizable matrix by a small perturbation; and the Jordan normal form theorem states that any matrix is uniquely the sum of a diagonalizable matrix and a nilpotent matrix. Over an algebraically closed field, diagonalizable matrices are equivalent to semi-simple matrices. (Wikipedia).

Diagonalizable matrix
Video thumbnail

Diagonalizability

Characterizations of Diagonalizability In this video, I define the notion of diagonalizability and show what it has to do with eigenvectors. Check out my Diagonalization playlist: https://www.youtube.com/playlist?list=PLJb1qAQIrmmCSovHY6cXzPMNSuWOwd9wB Subscribe to my channel: https://

From playlist Diagonalization

Video thumbnail

The Diagonalization of Matrices

This video explains the process of diagonalization of a matrix.

From playlist The Diagonalization of Matrices

Video thumbnail

Diagonal Matrices

This video defines a diagonal matrix and then explains how to determine the inverse of a diagonal matrix (if possible) and how to raise a diagonal matrix to a power. Site: mathispower4u.com Blog: mathispower4u.wordpress.com

From playlist Introduction to Matrices and Matrix Operations

Video thumbnail

Math 060 Fall 2017 112217C Diagonalization Part 2

Review: the matrix representation of a matrix with respect to an eigenvector basis is a diagonal matrix of eigenvalues. Definition: diagonalizable matrix. Alternate proof of the fact that a matrix is diagonalizable iff there exists an eigenvector basis. Exercise: diagonalize a matrix.

From playlist Course 4: Linear Algebra (Fall 2017)

Video thumbnail

Linear Algebra - Lecture 35 - Diagonalizable Matrices

In this lecture, we discuss what it means for a square matrix to be diagonalizable. We prove the Diagonalization Theorem, which tells us exactly when a matrix is diagonalizable.

From playlist Linear Algebra Lectures

Video thumbnail

Block Diagonal Matrices

Every operator on a finite-dimensional complex vector space has a matrix (with respect to some basis of the vector space) that is a block diagonal matrix, with each block itself an upper-triangular matrix that contains only one eigenvalue on the diagonal.

From playlist Linear Algebra Done Right

Video thumbnail

Diagonal Matrices are Freaking Awesome

When you have a diagonal matrix, everything in linear algebra is easy Learning Objectives: 1) Solve systems, compute eigenvalues, etc for Diagonal Matrices This video is part of a Linear Algebra course taught by Dr. Trefor Bazett at the University of Cincinnati

From playlist Linear Algebra (Full Course)

Video thumbnail

Determine the Product of a Matrix and Vector using the Diagonalization of the Matrix

This video explains how to use the diagonalization of a 2 by 2 matrix to find the product of a matrix and a vector given matrix P and D.

From playlist The Diagonalization of Matrices

Video thumbnail

Linear Algebra - Lecture 36 - Diagonalizing a Matrix

In this lecture, we work through some examples where we attempt to diagonalize a matrix. We also discuss a sufficient (but not necessary) condition for diagonalizability.

From playlist Linear Algebra Lectures

Video thumbnail

Linear Algebra 5.2 Diagonalization

My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul A. Roberts is supported in part by the grants NSF CAREER 1653602 and NSF DMS 2153803.

From playlist Linear Algebra

Video thumbnail

Not diagonalizable

Example of non-diagonalizable matrix. Exact conditions for a matrix to be (or not to be) diagonalizable. Algebraic and geometric multiplicity of an eigenvalue Check out my Eigenvalues playlist: https://www.youtube.com/watch?v=H-NxPABQlxI&list=PLJb1qAQIrmmC72x-amTHgG-H_5S19jOSf Subscribe

From playlist Eigenvalues

Video thumbnail

66 - Multiplicities of eigenvalues

Algebra 1M - international Course no. 104016 Dr. Aviv Censor Technion - International school of engineering

From playlist Algebra 1M

Video thumbnail

Math 060 Fall 2017 112917C Spectral Theorem for Hermitian Matrices

Review: A Hermitian matrix with all distinct eigenvalues is unitarily diagonalizable. Statement of Spectral Theorem: Every Hernitian matrix is unitarily diagonalizable. Lemma: Schur's Theorem (every matrix is unitarily upper triangularizable). Inductive proof of Schur's theorem. Proof

From playlist Course 4: Linear Algebra (Fall 2017)

Video thumbnail

Non diagonalization test

Test for non diagonalizability In this video, I state and prove the converse to the ultimate test of diagonalization: namely, a matrix is diagonalizable (if and) only if for every eigenvalue, the algebraic multiplicity equals the geometric multiplicity. Intuitively, this means if there a

From playlist Diagonalization

Video thumbnail

Derivative diagonalizable ?

Can you turn the derivative into a diagonal matrix? Watch this video and find out! Check out my Diagonalization playlist: https://www.youtube.com/playlist?list=PLJb1qAQIrmmCSovHY6cXzPMNSuWOwd9wB Subscribe to my channel: https://www.youtube.com/c/drpeyam

From playlist Diagonalization

Video thumbnail

Eigenspaces and Diagonal Matrices

Diagonal matrices. Eigenspaces. Conditions equivalent to diagonalizability.

From playlist Linear Algebra Done Right

Related pages

Modal matrix | Rotation matrix | Norm (mathematics) | Fibonacci number | Lebesgue measure | Schrödinger equation | Algebraically closed field | If and only if | Vector space | Characteristic polynomial | Linear algebra | Zariski topology | Spectral theorem | Block matrix | Matrix exponential | Lie theory | Algebraic variety | Hermitian matrix | Variational principle | Semi-simplicity | Defective matrix | Minimal polynomial (linear algebra) | Nilpotent | Diagonal matrix | Jordan–Chevalley decomposition | Separable polynomial | Determinant | Hypersurface | Eigenvalue algorithm | Nilpotent matrix | General linear group | Discriminant | Matrix similarity | Linear map | Scaling (geometry) | Dense set | Dimension (vector space) | Change of basis | Field (mathematics) | Square matrix | Real number | Orthonormal basis | Involution (mathematics) | Perturbation theory (quantum mechanics) | Projection (linear algebra) | Subset | Commuting matrices | Symmetric matrix | Unitary matrix | Basis (linear algebra) | Perturbation theory | Eigenvalues and eigenvectors | Orthogonal matrix | Hilbert space | Complex number | Jordan normal form | Triangular matrix | Semisimple operator | Skew-symmetric matrix | Matrix (mathematics) | Diagonalizable group | Endomorphism | Invertible matrix | Orthogonal diagonalization