# Singular value decomposition

Mathematical applications of the SVD include computing the pseudoinverse, matrix approximation, and determining the rank, range, and null space of a matrix. The SVD is also extremely useful in all areas of science, engineering, and statistics, such as signal processing, least squares fitting of data, and process control.

In particular, if **M** has a positive determinant, then **U** and **V**^{⁎} can be chosen to be both reflections, or both rotations. If the determinant is negative, exactly one of them will have to be a reflection. If the determinant is zero, each can be independently chosen to be of either type.

As shown in the figure, the singular values can be interpreted as the magnitude of the semiaxes of an ellipse in 2D. This concept can be generalized to n-dimensional Euclidean space, with the singular values of any *n* × *n* square matrix being viewed as the magnitude of the semiaxis of an n-dimensional ellipsoid. Similarly, the singular values of any *m* × *n* matrix can be viewed as the magnitude of the semiaxis of an n-dimensional ellipsoid in m-dimensional space, for example as an ellipse in a (tilted) 2D plane in a 3D space. Singular values encode magnitude of the semiaxis, while singular vectors encode direction. See below for further details.

Because **U** and **V** are unitary, we know that the columns **U**_{1}, ..., **U**_{m} of **U** yield an orthonormal basis of K^{m} and the columns **V**_{1}, ..., **V**_{n} of **V** yield an orthonormal basis of K^{n} (with respect to the standard scalar products on these spaces).

has a particularly simple description with respect to these orthonormal bases: we have

The geometric content of the SVD theorem can thus be summarized as follows: for every linear map *T* : *K ^{n}* →

*K*one can find orthonormal bases of K

^{m}^{n}and K

^{m}such that T maps the i-th basis vector of K

^{n}to a non-negative multiple of the i-th basis vector of K

^{m}, and sends the left-over basis vectors to zero. With respect to these bases, the map T is therefore represented by a diagonal matrix with non-negative real diagonal entries.

To get a more visual flavor of singular values and SVD factorization – at least when working on real vector spaces – consider the sphere S of radius one in **R**^{n}. The linear map T maps this sphere onto an ellipsoid in **R**^{m}. Non-zero singular values are simply the lengths of the semi-axes of this ellipsoid. Especially when *n* = *m*, and all the singular values are distinct and non-zero, the SVD of the linear map T can be easily analyzed as a succession of three consecutive moves: consider the ellipsoid *T*(*S*) and specifically its axes; then consider the directions in **R**^{n} sent by T onto these axes. These directions happen to be mutually orthogonal. Apply first an isometry **V**^{⁎} sending these directions to the coordinate axes of **R**^{n}. On a second move, apply an endomorphism **D** diagonalized along the coordinate axes and stretching or shrinking in each direction, using the semi-axes lengths of *T*(*S*) as stretching coefficients. The composition **D** ∘ **V**^{⁎} then sends the unit-sphere onto an ellipsoid isometric to *T*(*S*). To define the third and last move **U**, apply an isometry to this ellipsoid so as to carry it over *T*(*S*)^{[clarification needed]}. As can be easily checked, the composition **U** ∘ **D** ∘ **V**^{⁎} coincides with T.

As an exception, the left and right-singular vectors of singular value 0 comprise all unit vectors in the kernel and cokernel, respectively, of **M**, which by the rank–nullity theorem cannot be the same dimension if *m* ≠ *n*. Even if all singular values are nonzero, if *m* > *n* then the cokernel is nontrivial, in which case **U** is padded with *m* − *n* orthogonal vectors from the cokernel. Conversely, if *m* < *n*, then **V** is padded by *n* − *m* orthogonal vectors from the kernel. However, if the singular value of 0 exists, the extra columns of **U** or **V** already appear as left or right-singular vectors.

Non-degenerate singular values always have unique left- and right-singular vectors, up to multiplication by a unit-phase factor *e*^{iφ} (for the real case up to a sign). Consequently, if all singular values of a square matrix **M** are non-degenerate and non-zero, then its singular value decomposition is unique, up to multiplication of a column of **U** by a unit-phase factor and simultaneous multiplication of the corresponding column of **V** by the same unit-phase factor.
In general, the SVD is unique up to arbitrary unitary transformations applied uniformly to the column vectors of both **U** and **V** spanning the subspaces of each singular value, and up to arbitrary unitary transformations on vectors of **U** and **V** spanning the kernel and cokernel, respectively, of **M**.

The singular value decomposition is very general in the sense that it can be applied to any *m* × *n* matrix, whereas eigenvalue decomposition can only be applied to diagonalizable matrices. Nevertheless, the two decompositions are related.

Given an SVD of **M**, as described above, the following two relations hold:

The right-hand sides of these relations describe the eigenvalue decompositions of the left-hand sides. Consequently:

In the special case that **M** is a normal matrix, which by definition must be square, the spectral theorem says that it can be unitarily diagonalized using a basis of eigenvectors, so that it can be written **M** = **UDU**^{⁎} for a unitary matrix **U** and a diagonal matrix **D**. When **M** is also positive semi-definite, the decomposition **M** = **UDU**^{⁎} is also a singular value decomposition. Otherwise, it can be recast as an SVD by moving the phase of each σ_{i} to either its corresponding **V**_{i} or **U**_{i}. The natural connection of the SVD to non-normal matrices is through the polar decomposition theorem: **M** = **SR**, where **S** = **UΣU**^{⁎} is positive semidefinite and normal, and **R** = **UV**^{⁎} is unitary.

The singular value decomposition can be used for computing the pseudoinverse of a matrix. (Various authors use different notation for the pseudoinverse; here we use ^{†}.) Indeed, the pseudoinverse of the matrix **M** with singular value decomposition **M** = **UΣV**^{⁎} is

where **Σ**^{†} is the pseudoinverse of **Σ**, which is formed by replacing every non-zero diagonal entry by its reciprocal and transposing the resulting matrix. The pseudoinverse is one way to solve linear least squares problems.

A set of homogeneous linear equations can be written as **Ax** = **0** for a matrix **A** and vector **x**. A typical situation is that **A** is known and a non-zero **x** is to be determined which satisfies the equation. Such an **x** belongs to **A**'s null space and is sometimes called a (right) null vector of **A**. The vector **x** can be characterized as a right-singular vector corresponding to a singular value of **A** that is zero. This observation means that if **A** is a square matrix and has no vanishing singular value, the equation has no non-zero **x** as a solution. It also means that if there are several vanishing singular values, any linear combination of the corresponding right-singular vectors is a valid solution. Analogously to the definition of a (right) null vector, a non-zero **x** satisfying **x**^{⁎}**A** = **0**, with **x**^{⁎} denoting the conjugate transpose of **x**, is called a left null vector of **A**.

A total least squares problem seeks the vector **x** that minimizes the 2-norm of a vector **Ax** under the constraint ||**x**|| = 1. The solution turns out to be the right-singular vector of **A** corresponding to the smallest singular value.

Another application of the SVD is that it provides an explicit representation of the range and null space of a matrix **M**. The right-singular vectors corresponding to vanishing singular values of **M** span the null space of **M** and the left-singular vectors corresponding to the non-zero singular values of **M** span the range of **M**. For example, in the above example the null space is spanned by the last two rows of **V**^{⁎} and the range is spanned by the first three columns of **U**.

Here **U**_{i} and **V**_{i} are the i-th columns of the corresponding SVD matrices, σ_{i} are the ordered singular values, and each **A**_{i} is separable. The SVD can be used to find the decomposition of an image processing filter into separable horizontal and vertical filters. Note that the number of non-zero σ_{i} is exactly the rank of the matrix.

Separable models often arise in biological systems, and the SVD factorization is useful to analyze such systems. For example, some visual area V1 simple cells' receptive fields can be well described^{[1]} by a Gabor filter in the space domain multiplied by a modulation function in the time domain. Thus, given a linear filter evaluated through, for example, reverse correlation, one can rearrange the two spatial dimensions into one dimension, thus yielding a two-dimensional filter (space, time) which can be decomposed through SVD. The first column of **U** in the SVD factorization is then a Gabor while the first column of **V** represents the time modulation (or vice versa). One may then define an index of separability

which is the fraction of the power in the matrix M which is accounted for by the first separable matrix in the decomposition.^{[2]}

It is possible to use the SVD of a square matrix **A** to determine the orthogonal matrix **O** closest to **A**. The closeness of fit is measured by the Frobenius norm of **O** − **A**. The solution is the product **UV**^{⁎}.^{[3]} This intuitively makes sense because an orthogonal matrix would have the decomposition **UIV**^{⁎} where **I** is the identity matrix, so that if **A** = **UΣV**^{⁎} then the product **A** = **UV**^{⁎} amounts to replacing the singular values with ones. Equivalently, the solution is the unitary matrix **R** = **UV**^{⁎} of the Polar Decomposition **M** = **RP** = **P**'**R** in either order of stretch and rotation, as described above.

A similar problem, with interesting applications in shape analysis, is the orthogonal Procrustes problem, which consists of finding an orthogonal matrix **O** which most closely maps **A** to **B**. Specifically,

This problem is equivalent to finding the nearest orthogonal matrix to a given matrix **M** = **A**^{T}**B**.

The Kabsch algorithm (called Wahba's problem in other fields) uses SVD to compute the optimal rotation (with respect to least-squares minimization) that will align a set of points with a corresponding set of points. It is used, among other applications, to compare the structures of molecules.

The SVD and pseudoinverse have been successfully applied to signal processing,^{[4]} image processing^{[citation needed]} and big data (e.g., in genomic signal processing).^{[5]}^{[6]}^{[7]}^{[8]}

The SVD is also applied extensively to the study of linear inverse problems and is useful in the analysis of regularization methods such as that of Tikhonov. It is widely used in statistics, where it is related to principal component analysis and to Correspondence analysis, and in signal processing and pattern recognition. It is also used in output-only modal analysis, where the non-scaled mode shapes can be determined from the singular vectors. Yet another usage is latent semantic indexing in natural-language text processing.

One application of SVD to rather large matrices is in numerical weather prediction, where Lanczos methods are used to estimate the most linearly quickly growing few perturbations to the central numerical weather prediction over a given initial forward time period; i.e., the singular vectors corresponding to the largest singular values of the linearized propagator for the global weather over that time interval. The output singular vectors in this case are entire weather systems. These perturbations are then run through the full nonlinear model to generate an ensemble forecast, giving a handle on some of the uncertainty that should be allowed for around the current central prediction.

SVD has also been applied to reduced order modelling. The aim of reduced order modelling is to reduce the number of degrees of freedom in a complex system which is to be modeled. SVD was coupled with radial basis functions to interpolate solutions to three-dimensional unsteady flow problems.^{[11]}

Interestingly, SVD has been used to improve gravitational waveform modeling by the ground-based gravitational-wave interferometer aLIGO.^{[12]} SVD can help to increase the accuracy and speed of waveform generation to support gravitational-waves searches and update two different waveform models.

Singular value decomposition is used in recommender systems to predict people's item ratings.^{[13]} Distributed algorithms have been developed for the purpose of calculating the SVD on clusters of commodity machines.^{[14]}

Low-rank SVD has been applied for hotspot detection from spatiotemporal data with application to disease outbreak detection.^{[15]} A combination of SVD and higher-order SVD also has been applied for real time event detection from complex data streams (multivariate data with space and time dimensions) in Disease surveillance.^{[16]}

An eigenvalue λ of a matrix **M** is characterized by the algebraic relation **Mu** = *λ***u**. When **M** is Hermitian, a variational characterization is also available. Let **M** be a real *n* × *n* symmetric matrix. Define

for some real number λ. The nabla symbol, ∇, is the del operator (differentiation with respect to **x**). Using the symmetry of **M** we obtain

Therefore **Mu** = *λ***u**, so **u** is a unit length eigenvector of **M**. For every unit length eigenvector **v** of **M** its eigenvalue is *f*(**v**), so λ is the largest eigenvalue of **M**. The same calculation performed on the orthogonal complement of *u* gives the next largest eigenvalue and so on. The complex Hermitian case is similar; there *f*(**x**) = **x*** *M* **x** is a real-valued function of 2*n* real variables.

Singular values are similar in that they can be described algebraically or from variational principles. Although, unlike the eigenvalue case, Hermiticity, or symmetry, of **M** is no longer required.

This section gives these two arguments for existence of singular value decomposition.

where the subscripts on the identity matrices are used to remark that they are of different dimensions.

Notice the argument could begin with diagonalizing **MM**^{⁎} rather than **M**^{⁎}**M** (This shows directly that **MM**^{⁎} and **M**^{⁎}**M** have the same non-zero eigenvalues).

The singular values can also be characterized as the maxima of **u**^{T}**Mv**, considered as a function of **u** and **v**, over particular subspaces. The singular vectors are the values of **u** and **v** where these maxima are attained.

Consider the function σ restricted to *S*^{m−1} × *S*^{n−1}. Since both *S*^{m−1} and *S*^{n−1} are compact sets, their product is also compact. Furthermore, since σ is continuous, it attains a largest value for at least one pair of vectors **u** ∈ *S*^{m−1} and **v** ∈ *S*^{n−1}. This largest value is denoted *σ*_{1} and the corresponding vectors are denoted **u**_{1} and **v**_{1}. Since *σ*_{1} is the largest value of *σ*(**u**, **v**) it must be non-negative. If it were negative, changing the sign of either **u**_{1} or **v**_{1} would make it positive and therefore larger.

**Statement.** **u**_{1}, **v**_{1} are left and right-singular vectors of **M** with corresponding singular value *σ*_{1}.

**Proof.** Similar to the eigenvalues case, by assumption the two vectors satisfy the Lagrange multiplier equation:

More singular vectors and singular values can be found by maximizing *σ*(**u**, **v**) over normalized **u**, **v** which are orthogonal to **u**_{1} and **v**_{1}, respectively.

The singular value decomposition can be computed using the following observations:

The SVD of a matrix **M** is typically computed by a two-step procedure. In the first step, the matrix is reduced to a bidiagonal matrix. This takes O(*mn*^{2}) floating-point operations (flop), assuming that *m* ≥ *n*. The second step is to compute the SVD of the bidiagonal matrix. This step can only be done with an iterative method (as with eigenvalue algorithms). However, in practice it suffices to compute the SVD up to a certain precision, like the machine epsilon. If this precision is considered constant, then the second step takes O(*n*) iterations, each costing O(*n*) flops. Thus, the first step is more expensive, and the overall cost is O(*mn*^{2}) flops (Trefethen & Bau III 1997, Lecture 31).

The first step can be done using Householder reflections for a cost of 4*mn*^{2} − 4*n*^{3}/3 flops, assuming that only the singular values are needed and not the singular vectors. If *m* is much larger than *n* then it is advantageous to first reduce the matrix **M** to a triangular matrix with the QR decomposition and then use Householder reflections to further reduce the matrix to bidiagonal form; the combined cost is 2*mn*^{2} + 2*n*^{3} flops (Trefethen & Bau III 1997, Lecture 31).

The second step can be done by a variant of the QR algorithm for the computation of eigenvalues, which was first described by Golub & Kahan (1965). The LAPACK subroutine DBDSQR^{[18]} implements this iterative method, with some modifications to cover the case where the singular values are very small (Demmel & Kahan 1990). Together with a first step using Householder reflections and, if appropriate, QR decomposition, this forms the DGESVD^{[19]} routine for the computation of the singular value decomposition.

The same algorithm is implemented in the GNU Scientific Library (GSL). The GSL also offers an alternative method that uses a one-sided Jacobi orthogonalization in step 2 (GSL Team 2007). This method computes the SVD of the bidiagonal matrix by solving a sequence of 2 × 2 SVD problems, similar to how the Jacobi eigenvalue algorithm solves a sequence of 2 × 2 eigenvalue methods (Golub & Van Loan 1996, §8.6.3). Yet another method for step 2 uses the idea of divide-and-conquer eigenvalue algorithms (Trefethen & Bau III 1997, Lecture 31).

There is an alternative way that does not explicitly use the eigenvalue decomposition.^{[20]} Usually the singular value problem of a matrix **M** is converted into an equivalent symmetric eigenvalue problem such as **M M**^{⁎}, **M**^{⁎}**M**, or

The approaches that use eigenvalue decompositions are based on the QR algorithm, which is well-developed to be stable and fast. Note that the singular values are real and right- and left- singular vectors are not required to form similarity transformations. One can iteratively alternate between the QR decomposition and the LQ decomposition to find the real diagonal Hermitian matrices. The QR decomposition gives **M** ⇒ **Q R** and the LQ decomposition of **R** gives **R** ⇒ **L P**^{⁎}. Thus, at every iteration, we have **M** ⇒ **Q L P**^{⁎}, update **M** ⇐ **L** and repeat the orthogonalizations. Eventually,^{[clarification needed]} this iteration between QR decomposition and LQ decomposition produces left- and right- unitary singular matrices. This approach cannot readily be accelerated, as the QR algorithm can with spectral shifts or deflation. This is because the shift method is not easily defined without using similarity transformations. However, this iterative approach is very simple to implement, so is a good choice when speed does not matter. This method also provides insight into how purely orthogonal/unitary transformations can obtain the SVD.

In applications it is quite unusual for the full SVD, including a full unitary decomposition of the null-space of the matrix, to be required. Instead, it is often sufficient (as well as faster, and more economical for storage) to compute a reduced version of the SVD. The following can be distinguished for an *m*×*n* matrix *M* of rank *r*:

the matrices *U*_{k} and *V*_{k} contain only the first *k* columns of *U* and *V*, and Σ_{k} contains only the first *k* singular values from Σ. The matrix *U*_{k} is thus *m*×*k*, Σ_{k} is *k*×*k* diagonal, and *V*_{k}* is *k*×*n*.

The thin SVD uses significantly less space and computation time if *k* ≪ max(*m*, *n*). The first stage in its calculation will usually be a QR decomposition of *M*, which can make for a significantly quicker calculation in this case.

Only the *r* column vectors of *U* and *r* row vectors of *V** corresponding to the non-zero singular values Σ_{r} are calculated. The remaining vectors of *U* and *V** are not calculated. This is quicker and more economical than the thin SVD if *r* ≪ min(*m*, *n*). The matrix *U*_{r} is thus *m*×*r*, Σ_{r} is *r*×*r* diagonal, and *V*_{r}* is *r*×*n*.

Only the *t* column vectors of *U* and *t* row vectors of *V** corresponding to the *t* largest singular values Σ_{t} are calculated. The rest of the matrix is discarded. This can be much quicker and more economical than the compact SVD if *t*≪*r*. The matrix *U*_{t} is thus *m*×*t*, Σ_{t} is *t*×*t* diagonal, and *V*_{t}* is *t*×*n*.

The sum of the *k* largest singular values of *M* is a matrix norm, the Ky Fan *k*-norm of *M*.^{[22]}

The first of the Ky Fan norms, the Ky Fan 1-norm, is the same as the operator norm of *M* as a linear operator with respect to the Euclidean norms of *K*^{m} and *K*^{n}. In other words, the Ky Fan 1-norm is the operator norm induced by the standard *ℓ*^{2} Euclidean inner product. For this reason, it is also called the operator 2-norm. One can easily verify the relationship between the Ky Fan 1-norm and singular values. It is true in general, for a bounded operator *M* on (possibly infinite-dimensional) Hilbert spaces

But, in the matrix case, (*M* M*)^{1/2} is a normal matrix, so ||*M* M*||^{1/2} is the largest eigenvalue of (*M* M*)^{1/2}, i.e. the largest singular value of *M*.

The last of the Ky Fan norms, the sum of all singular values, is the trace norm (also known as the 'nuclear norm'), defined by ||*M*|| = Tr[(*M* M*)^{1/2}] (the eigenvalues of *M* M* are the squares of the singular values).

The singular values are related to another norm on the space of operators. Consider the Hilbert–Schmidt inner product on the *n* × *n* matrices, defined by

where σ_{i} are the singular values of **M**. This is called the **Frobenius norm**, **Schatten 2-norm**, or **Hilbert–Schmidt norm** of **M**. Direct calculation shows that the Frobenius norm of **M** = (*m*_{ij}) coincides with:

In addition, the Frobenius norm and the trace norm (the nuclear norm) are special cases of the Schatten norm.

Two types of tensor decompositions exist, which generalise the SVD to multi-way arrays. One of them decomposes a tensor into a sum of rank-1 tensors, which is called a tensor rank decomposition. The second type of decomposition computes the orthonormal subspaces associated with the different factors appearing in the tensor product of vector spaces in which the tensor lives. This decomposition is referred to in the literature as the higher-order SVD (HOSVD) or Tucker3/TuckerM. In addition, multilinear principal component analysis in multilinear subspace learning involves the same mathematical operations as Tucker decomposition, being used in a different context of dimensionality reduction.

The singular values of a matrix *A* are uniquely defined and are invariant with respect to left and/or right unitary transformations of *A*. In other words, the singular values of *UAV*, for unitary *U* and *V*, are equal to the singular values of *A*. This is an important property for applications in which it is necessary to preserve Euclidean distances and invariance with respect to rotations.

The Scale-Invariant SVD, or SI-SVD,^{[24]} is analogous to the conventional SVD except that its uniquely-determined singular values are invariant with respect to diagonal transformations of *A*. In other words, the singular values of *DAE*, for invertible diagonal matrices *D* and *E*, are equal to the singular values of *A*. This is an important property for applications for which invariance to the choice of units on variables (e.g., metric versus imperial units) is needed.

Tensor product (TP) model transformation numerically reconstruct the HOSVD of functions. For further details please visit:

The factorization **M** = **UΣV**^{⁎} can be extended to a bounded operator *M* on a separable Hilbert space *H*. Namely, for any bounded operator *M*, there exist a partial isometry *U*, a unitary *V*, a measure space (*X*, *μ*), and a non-negative measurable *f* such that

This can be shown by mimicking the linear algebraic argument for the matricial case above. *VT*_{f}*V** is the unique positive square root of *M*M*, as given by the Borel functional calculus for self-adjoint operators. The reason why *U* need not be unitary is because, unlike the finite-dimensional case, given an isometry *U*_{1} with nontrivial kernel, a suitable *U*_{2} may not be found such that

As for matrices, the singular value factorization is equivalent to the polar decomposition for operators: we can simply write

and notice that *U V** is still a partial isometry while *VT*_{f}*V** is positive.

Compact operators on a Hilbert space are the closure of finite-rank operators in the uniform operator topology. The above series expression gives an explicit such representation. An immediate consequence of this is:

The singular value decomposition was originally developed by differential geometers, who wished to determine whether a real bilinear form could be made equal to another by independent orthogonal transformations of the two spaces it acts on. Eugenio Beltrami and Camille Jordan discovered independently, in 1873 and 1874 respectively, that the singular values of the bilinear forms, represented as a matrix, form a complete set of invariants for bilinear forms under orthogonal substitutions. James Joseph Sylvester also arrived at the singular value decomposition for real square matrices in 1889, apparently independently of both Beltrami and Jordan. Sylvester called the singular values the *canonical multipliers* of the matrix *A*. The fourth mathematician to discover the singular value decomposition independently is Autonne in 1915, who arrived at it via the polar decomposition. The first proof of the singular value decomposition for rectangular and complex matrices seems to be by Carl Eckart and Gale J. Young in 1936;^{[25]} they saw it as a generalization of the principal axis transformation for Hermitian matrices.

Practical methods for computing the SVD date back to Kogbetliantz in 1954–1955 and Hestenes in 1958,^{[26]} resembling closely the Jacobi eigenvalue algorithm, which uses plane rotations or Givens rotations. However, these were replaced by the method of Gene Golub and William Kahan published in 1965,^{[27]} which uses Householder transformations or reflections. In 1970, Golub and Christian Reinsch^{[28]} published a variant of the Golub/Kahan algorithm that is still the one most-used today.