## Cholesky Decomposition with R Example

method of decomposing a positive-definite matrix. A positive-definite matrix is defined as a symmetric matrix where for all possible vectors $$x$$, $$x'Ax > 0$$. Cholesky decomposition and other decomposition methods are important as it is not often feasible to perform matrix computations explicitly.

Cholesky decomposition, also known as Cholesky factorization, is a method of decomposing a positive-definite matrix. A positive-definite matrix is defined as a symmetric matrix where for all possible vectors $$x$$, $$x'Ax > 0$$. Cholesky decomposition and other decomposition methods are important as it is not often feasible to perform matrix computations explicitly. Some applications of Cholesky decomposition include solving systems of linear equations, Monte Carlo simulation, and Kalman filters.

Cholesky decomposition factors a positive-definite matrix $$A$$ into:

$$A = LL^T$$

Where $$L$$ is a lower triangular matrix. $$L$$ is known as the Cholesky factor of $$A$$ and can be interpreted as the square root of a positive-definite matrix.

## How to Decompose a Matrix with Cholesky Decomposition

There are many methods for computing a matrix decomposition with the Cholesky approach. This post takes a similar approach to this implementation.

We define the decomposed matrix as $$L$$. Thus $$L_{k-1}$$ represents the $$k-1 \times k-1$$ upper left corner of $$L$$. $$a_k$$ and $$l_k$$ denote the first $$k - 1$$ entries in column $$k$$ of $$A$$ and $$L$$, respectively. $$a_{kk}$$ and $$l_{kk}$$ are defined as the entries of $$A$$ and $$L$$.

The steps in factoring the matrix are as follows:

1. Compute $$L_1 = \sqrt{a_{11}}$$
2. For $$k = 2, \dots, n$$:

3. Find $$L_{k-1} l_k = a_k$$ for $$l_k$$

4. $$l_{kk} = \sqrt{a_{kk} - l_k^T l_k}$$
5. $$L_k = \begin{bmatrix} L_{k-1} & 0 \\ l_k^T & l_{kk}\end{bmatrix}$$

## An Example of Cholesky Decomposition

Consider the following matrix $$A$$.

$$A = \begin{bmatrix} 3 & 4 & 3 \\ 4 & 8 & 6 \\ 3 & 6 & 9 \end{bmatrix}$$

The matrix $$A$$ above is taken from Exercise 2.16 in the book Methods of Multivariate Analysis by Alvin Rencher.

Begin by finding $$L_1$$.

$$L_1 = \sqrt{a_{11}} = \sqrt{3} = 1.732051$$

Next we find $$l_2$$

$$l_2 = \frac{a_{21}}{L_1} = \frac{4}{\sqrt{3}} = 2.309401$$

Then $$l_{22}$$ can be computed.

$$l_{22} = \sqrt{a_{22} - l_2^T l_2} = \sqrt{8 - 2.309401^2} = 1.632993$$

We now have the $$L_2$$ matrix:

$$L_2 = \begin{bmatrix} L_1 & 0 \\ l_2^T & l_{22} \end{bmatrix} = \begin{bmatrix} 1.732051 & 0 \\ 2.309401 & 1.632993 \end{bmatrix}$$

Since the matrix is $$3 \times 3$$, we only require one more iteration.

With $$L_2$$ computed, $$l_3$$ can be found:

$$l_3 = \frac{a_3}{L_2} = a_3 L_2^{-1} = \begin{bmatrix} 1.732051 & 0 \\ 2.309401 & 1.632993 \end{bmatrix}^{-1} \begin{bmatrix} 3 \\ 6 \end{bmatrix}$$
$$l_3 = \begin{bmatrix} 1.7320508 \\ 1.224745 \end{bmatrix}$$

$$l_{33}$$ is then found:

$$l_{33} = \sqrt{a_{33} - l_3^T l_3} = \sqrt{9 - \begin{bmatrix}1.7320508 & 1.224745\end{bmatrix} \begin{bmatrix}1.7320508 \\ 1.224745\end{bmatrix}} = 2.12132$$

Which gives us the $$L_3$$ matrix:

$$L_3 = \begin{bmatrix} 1.7320508 & 0 & 0 \\ 2.309401 & 1.632993 & 0 \\ 1.7320508 & 1.224745 & 2.12132 \end{bmatrix}$$

The $$L_3$$ matrix can then be taken as the solution. Transposing the decomposition changes the matrix into an upper triangular matrix.

## Cholesky Decomposition in R

The function chol() performs Cholesky decomposition on a positive-definite matrix. We define the matrix $$A$$ as follows.

A = as.matrix(data.frame(c(3,4,3),c(4,8,6),c(3,6,9)))
colnames(A) <- NULL
A

##      [,1] [,2] [,3]
## [1,]    3    4    3
## [2,]    4    8    6
## [3,]    3    6    9


Then factor the matrix with the chol() function.

A.chol <- chol(A)
A.chol

##          [,1]     [,2]     [,3]
## [1,] 1.732051 2.309401 1.732051
## [2,] 0.000000 1.632993 1.224745
## [3,] 0.000000 0.000000 2.121320


The chol() function returns an upper triangular matrix. Transposing the decomposed matrix yields a lower triangular matrix as in our result above.

t(A.chol)

##          [,1]     [,2]    [,3]
## [1,] 1.732051 0.000000 0.00000
## [2,] 2.309401 1.632993 0.00000
## [3,] 1.732051 1.224745 2.12132


Our result above matches the output of the chol() function.

We can also show the identity $$A = LL^T$$ with the result.

t(A.chol) %*% A.chol

##      [,1] [,2] [,3]
## [1,]    3    4    3
## [2,]    4    8    6
## [3,]    3    6    9


## Summary

Cholesky decomposition is frequently utilized when direct computation of a matrix is not optimal. The method is employed in a variety of applications such as multivariate analysis due to its relatively efficient nature and stability.