Linear Algebra

Linear equations, eigenvalues, singular values, decomposition, matrix operations, matrix structure

Linear algebra functions in MATLAB^® provide fast, numerically robust matrix calculations. Capabilities include a variety of matrix factorizations, linear equation solving, computation of eigenvalues or singular values, and more. For an introduction, see Matrices in the MATLAB Environment.

Functions

expand all

Linear Equations

`mldivide`	Solve systems of linear equations Ax = B for x
`mrdivide`	Solve systems of linear equations xA = B for x
`pagemldivide`	Page-wise left matrix divide
`pagemrdivide`	Page-wise right matrix divide
`decomposition`	Matrix decomposition for solving linear systems
`lsqminnorm`	Minimum norm least-squares solution to linear equation
`linsolve`	Solve linear system of equations
`inv`	Matrix inverse
`pageinv`	Page-wise matrix inverse
`pinv`	Moore-Penrose pseudoinverse
`lscov`	Least-squares solution in presence of known covariance
`lsqnonneg`	Solve nonnegative linear least-squares problem
`sylvester`	Solve Sylvester equation AX + XB = C for X

Eigenvalues and Singular Values

`eig`	Eigenvalues and eigenvectors
`pageeig`	Page-wise eigenvalues and eigenvectors
`eigs`	Subset of eigenvalues and eigenvectors
`balance`	Diagonal scaling to improve eigenvalue accuracy
`svd`	Singular value decomposition
`pagesvd`	Page-wise singular value decomposition
`svds`	Subset of singular values and vectors
`svdsketch`	Compute SVD of low-rank matrix sketch
`gsvd`	Generalized singular value decomposition
`ordeig`	Eigenvalues of quasitriangular matrices
`ordqz`	Reorder eigenvalues in QZ factorization
`ordschur`	Reorder eigenvalues in Schur factorization
`polyeig`	Polynomial eigenvalue problem
`qz`	QZ factorization for generalized eigenvalues
`hess`	Hessenberg form of matrix
`schur`	Schur decomposition
`rsf2csf`	Convert real Schur form to complex Schur form
`cdf2rdf`	Convert complex diagonal form to real block diagonal form

Matrix Decomposition

`lu`	LU matrix factorization
`ldl`	Block LDL' factorization for Hermitian indefinite matrices
`chol`	Cholesky factorization
`cholupdate`	Rank 1 update to Cholesky factorization
`qr`	QR decomposition
`qrdelete`	Remove column or row from QR factorization
`qrinsert`	Insert column or row into QR factorization
`qrupdate`	Rank 1 update to QR factorization
`planerot`	Givens plane rotation

Matrix Operations

`transpose`	Transpose vector or matrix
`ctranspose`	Complex conjugate transpose
`pagetranspose`	Page-wise transpose
`pagectranspose`	Page-wise complex conjugate transpose
`mtimes`	Matrix multiplication
`pagemtimes`	Page-wise matrix multiplication
`mpower`	Matrix power
`sqrtm`	Matrix square root
`expm`	Matrix exponential
`logm`	Matrix logarithm
`funm`	Evaluate general matrix function
`kron`	Kronecker tensor product
`cross`	Cross product
`dot`	Dot product

Matrix Structure

`bandwidth`	Lower and upper matrix bandwidth
`tril`	Lower triangular part of matrix
`triu`	Upper triangular part of matrix
`isbanded`	Determine if matrix is within specific bandwidth
`isdiag`	Determine if matrix is diagonal
`ishermitian`	Determine if matrix is Hermitian or skew-Hermitian
`issymmetric`	Determine if matrix is symmetric or skew-symmetric
`istril`	Determine if matrix is lower triangular
`istriu`	Determine if matrix is upper triangular

Matrix Properties

`norm`	Vector and matrix norms
`pagenorm`	Page-wise matrix or vector norm
`normest`	2-norm estimate
`vecnorm`	Vector-wise norm
`cond`	Condition number for inversion
`condest`	1-norm condition number estimate
`rcond`	Reciprocal condition number
`condeig`	Condition number with respect to eigenvalues
`det`	Matrix determinant
`null`	Null space of matrix
`orth`	Orthonormal basis for range of matrix
`rank`	Rank of matrix
`rref`	Reduced row echelon form (Gauss-Jordan elimination)
`trace`	Sum of diagonal elements
`subspace`	Angle between two subspaces

Topics

Matrices in the MATLAB Environment
Matrix creation and basic operations.
Systems of Linear Equations
Solve several types of systems of linear equations.
Eigenvalues
Eigenvalue and eigenvector computation.
Singular Values
Singular value decomposition (SVD).
Factorizations
Common matrix factorizations (Cholesky, LU, QR).
Matrix Exponentials
This example shows three of the 19 ways to compute the exponential of a matrix.
Determine Whether Matrix Is Symmetric Positive Definite
This topic explains how to use the chol and eig functions to determine whether a matrix is symmetric positive definite (a symmetric matrix with all positive eigenvalues).
LAPACK in MATLAB
LAPACK provides a foundation of routines for linear algebra functions and matrix computations in MATLAB.

Related Information

Matrix Methods of Linear Algebra (MathWorks Teaching Resources)

Featured Examples

Basic Matrix Operations

Basic techniques and functions for working with matrices in the MATLAB® language.

Open Live Script

Image Compression with Low-Rank SVD

Use svdsketch to compress an image while preserving important features of the image.

Open Live Script