Cholesky decomposition

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In mathematics, the Cholesky decomposition is named after André-Louis Cholesky, who found that a symmetric positive-definite matrix can be decomposed into a lower triangular matrix and the transpose of the lower triangular matrix. The lower triangular matrix is the Cholesky triangle of the original, positive-definite matrix. Cholesky's result has since been extended to matrices with complex entries.

Any square matrix A with non-zero pivots can be written as the product of a lower triangular matrix L and an upper triangular matrix U; this is called the LU decomposition. However, if A is symmetric and positive definite, we can choose the factors such that U is the transpose of L, and this is called the Cholesky decomposition. Both the LU and the Cholesky decomposition are used to solve systems of linear equations. When it is applicable, the Cholesky decomposition is twice as efficient as the LU decomposition.

1 Statement
2 Applications
3 Computing the Cholesky decomposition
4 Proof for positive semi-definite matrices
5 Generalization
6 See also
7 References
8 External links

[edit] Statement

If A is Hermitian and positive definite, then A can be decomposed as

$\mathbf{A} = \mathbf{L} \mathbf{L}^{*},$

where L is a lower triangular matrix with strictly positive diagonal entries, and L^* denotes the conjugate transpose of L. This is the Cholesky decomposition.

The Cholesky decomposition is unique: given a Hermitian, positive-definite matrix A, there is only one lower triangular matrix L with strictly positive diagonal entries such that A = LL^*. The converse holds trivially: if A can be written as LL^* for any invertible L, lower triangular or otherwise, then A is Hermitian and positive definite.

The requirement that L have strictly positive diagonal entries can be dropped to extend the factorization to the positive semidefinite case. The statement then reads: a square matrix A has a Cholesky decomposition if and only if A is Hermitian and positive semi-definite. Cholesky factorizations for positive semidefinite matrices are not unique in general.

In the special case that A is a symmetric positive-definite matrix with real entries, L can be assumed to have real entries as well.

[edit] Applications

The Cholesky decomposition is mainly used for the numerical solution of linear equations Ax = b. If A is symmetric and positive definite, then we can solve Ax = b by first computing the Cholesky decomposition A = LL^T, then solving Ly = b for y, and finally solving L^Tx = y for x.

Systems of the form Ax = b with A symmetric and positive definite arise quite often in applications. For instance, the normal equations in linear least squares problems are of this form. It may also happen that matrix A comes from an energy functional which must be positive from physical considerations; this happens frequently in the numerical solution of partial differential equations.

The Cholesky decomposition is commonly used in the Monte Carlo method for simulating systems with multiple correlated variables: The matrix of inter-variable correlations is decomposed, to give the lower-triangular L. Applying this to a vector of uncorrelated simulated shocks, u, produces a shock vector Lu with the covariance properties of the system being modelled.

Unscented Kalman filters commonly use the Cholesky decomposition to choose a set of so-called sigma points. The Kalman filter tracks the average state of a system as a vector x of length N and covariance as an N-by-N matrix P. The matrix P is always positive semi-definite, and can be decomposed into LL^T. The columns of L can be added and subtracted from the mean x to form a set of 2N vectors called sigma points. These sigma points completely capture the mean and covariance of the system state.

[edit] Computing the Cholesky decomposition

There are various methods for calculating the Cholesky decomposition. The algorithms described below all involve about n³/3 FLOPs, where n is the size of the matrix A. Hence, they are half the cost of the LU decomposition, which uses 2n³/3 FLOPs.

Which of the algorithms below is faster depends on the details on the implementation. Generally, the first algorithm will be slightly slower because it accesses the data in a more irregular manner.

[edit] The Cholesky algorithm

The Cholesky algorithm, used to calculate the decomposition matrix L, is a modified version of the Gauss algorithm.

The recursive algorithm starts with i := 1 and

$\mathbf{A}^{(1)} := \mathbf{A}.$

At step i, the matrix A⁽ⁱ⁾ has the following form:

$\mathbf{A}^{(i)} = \begin{pmatrix} \mathbf{I}_{i-1} & 0 & 0 \\ 0 & a_{i,i} & \mathbf{b}_{i}^{*} \\ 0 & \mathbf{b}_{i} & \mathbf{B}^{(i)} \end{pmatrix},$

where I_i−1 denotes the identity matrix of dimension i − 1.

If we now define the matrix L_i by

$\mathbf{L}_{i} := \begin{pmatrix} \mathbf{I}_{i-1} & 0 & 0 \\ 0 & \sqrt{a_{i,i}} & 0 \\ 0 & \frac{1}{\sqrt{a_{i,i}}} \mathbf{b}_{i} & \mathbf{I}_{n-i} \end{pmatrix},$

then we can write A⁽ⁱ⁾ as

$\mathbf{A}^{(i)} = \mathbf{L}_{i} \mathbf{A}^{(i+1)} \mathbf{L}_{i}^{*}$

where

$\mathbf{A}^{(i+1)} = \begin{pmatrix} \mathbf{I}_{i-1} & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & \mathbf{B}^{(i)} - \frac{1}{a_{i,i}} \mathbf{b}_{i} \mathbf{b}_{i}^{*} \end{pmatrix}.$

Note that b_i b_i^* is an outer product, therefore this algorithm is called the outer product version in (Golub & Van Loan).

We repeat this for i from 1 to n. After n steps, we get A⁽ⁿ⁺¹⁾ = I. Hence, the lower triangular matrix L we are looking for is calculated as

$\mathbf{L} := \mathbf{L}_{1} \mathbf{L}_{2} \dots \mathbf{L}_{n}.$

[edit] The Cholesky-Banachiewicz and Cholesky-Crout algorithms

If we write out the equation A = LL^*, we obtain the following formula for the entries of L:

$l_{i,j} = \frac{1}{l_{j,j}} \left( a_{i,j} - \sum_{k=1}^{j-1} l_{i,k} l_{j,k} \right), \qquad\mbox{for } i>j.$

$l_{i,i} = \sqrt{ a_{i,i} - \sum_{k=1}^{i-1} l_{i,k}^2 }.$

The expression under the square root is always positive if A is real and positive-definite.

So we can compute the (i, j) entry if we know the entries to the left and above. The computation is usually arranged in either of the following orders.

The Cholesky-Banachiewicz algorithm starts form the upper left corner of the matrix L and proceeds to calculate the matrix row by row.
The Cholesky-Crout algorithm starts from the upper left corner of the matrix L and proceeds to calculate the matrix column by column.

[edit] Stability of the computation

Suppose that we want to solve a well-conditioned system of linear equations. If the LU decomposition is used, then the algorithm is unstable unless we use some sort of pivoting strategy. In the latter case, the error depends on the so-called growth factor of the matrix, which is usually (but not always) small.

Now, suppose that the Cholesky decomposition is applicable. As mentioned above, the algorithm will be twice as fast. Furthermore, no pivoting is necessary and the error will always be small. Specifically, if we want to solve Ax = b, and y denotes the computed solution, then y solves the disturbed system (A + E)y = b where

$\|\mathbf{E}\|_2 \le c_n \varepsilon \|\mathbf{A}\|_2.$

Here, || ||₂ is the matrix 2-norm, c_n is a small constant depending on n, and ε denotes the unit roundoff.

There is one small problem with the Cholesky decomposition. Note that we must compute square roots in order to find the Cholesky decomposition. If the matrix is real symmetric and positive definite, then the numbers under the square roots are always positive in exact arithmetic. Unfortunately, the numbers can become negative because of round-off errors, in which case the algorithm cannot continue. However, this can only happen if the matrix is very ill-conditioned.

[edit] Proof for positive semi-definite matrices

The above algorithms show that every positive definite matrix A has a Cholesky decomposition. This result can be extended to the positive semi-definite case by a limiting argument. The argument is not fully constructive, i.e., it gives no explicit numerical algorithms for computing Cholesky factors.

If A is an n-by-n positive semi-definite matrix, then the sequence $\{ A_k = A + \begin{matrix} \frac{1}{k} \end{matrix} I_n \}$ consists of positive definite matrices. (This is an immediate consequence of, for example, the spectral mapping theorem for the polynomial functional calculus.) We also have

$A_k \rightarrow A \,$

in the usual operator norm. From the positive definite case, each $A k$ has Cholesky decomposition $A_k = L_k L_k ^*$ . By property of the operator norm, we have

$\| L_k \|^2 = \| L_k L_k ^* \| = \| A_k \|.$

So ${L k}$ is a bounded set in the Banach space of operators, therefore precompact (because the underlying vector space is finite dimensional). Consequently it has a convergent subsequence, also denoted by ${L k}$ , which converges to some L. It can be easily checked that this L has the desired properties, i.e. $A = L L *$ and L is lower triangular with non-negative diagonal entries. For all x and y,

$\langle Ax, y \rangle = \langle \lim A_k x, y \rangle = \langle \lim L_k L_k^* x, y \rangle = \langle L L^*x, y \rangle.$

That L must be lower triangular with non-negative diagonal entries can be deduced by again invoking the assumption that the underlying vector space is finite dimensional, which in turn implies all topologies on the space of operators are equivalent.

[edit] Generalization

The Cholesky factorization can be generalized to (infinite) matrices with operator entries. Let ${H n}$ be a sequence of Hilbert spaces. Consider the operator matrix

$A = \begin{bmatrix} A_{11} & A_{12} & A_{13} & \; \\ A_{12}^* & A_{22} & A_{23} & \; \\ A_{13}^* & A_{23}^* & A_{33} & \; \\ \; & \; & \; & \ddots \end{bmatrix}$

acting on the direct sum $H = \oplus _n H_n$ , where $\; A_{ij}$ is a bounded operator from $\; H_j$ to $\; H_i$ . If A is positive (semidefinite) in the sense that for all finite k and for any $h \in \oplus _{n = 1 }^k H_k$ , we have $\langle h, A h \rangle \geq 0$ , then there exists a lower triangular operator matrix L such that $A = L L *$ .

[edit] See also

[edit] References

Gene H. Golub and Charles F. Van Loan, Matrix computations (3rd ed.), Section 4.2, Johns Hopkins University Press. ISBN 0-8018-5414-8.
Roger A. Horn and Charles R. Johnson. Matrix Analysis, Section 7.2. Cambridge University Press, 1985. ISBN 0-521-38632-2.
S. J. Julier and J.K. Uhlmann, "A new extension of the Kalman filter to nonlinear systems," in Proc. AeroSense: 11th Int. Symp. Aerospace/Defence Sensing, Simulation and Controls, 1997, pp. 182-193.

[edit] External links

Information

Cholesky Decomposition on PlanetMath
Cholesky Decomposition, The Data Analysis BriefBook
Cholesky Matrix, riskglossary.com
Cholesky Decomposition on www.math-linux.com

Computer code

LAPACK is a collection of FORTRAN subroutines for solving dense linear algebra problems
ALGLIB includes a partial port of the LAPACK to C++, C#, Delphi, etc.
Chapter 2.9 of "Numerical Recipes in C" gives information about implementation of Cholesky algorithm in C.
Cholesky Factorization in VB, vbnumericalmethods.com

Online calculators

Online Matrix Calculator Performs Cholesky decomposition of matrices online.

Use of the matrix in simulation

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Categories: Numerical linear algebra | Operator theory