Efficient Cholesky decomposition of low-rank updates

A short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates

Suppose we’re given a positive semidefinite (PSD) matrix $\mathbf{A} \in \mathbb{R}^{N \times N}$ to which we wish to update by some low-rank matrix $\mathbf{U} \mathbf{U}^\top \in \mathbb{R}^{N \times N}$, $$\mathbf{B} \triangleq \mathbf{A} + \mathbf{U} \mathbf{U}^\top,$$ where the update factor matrix $\mathbf{U} \in \mathbb{R}^{N \times M}$. To be more precise, the low-rank update is rank-$M$ for some $M \ll N$.

What is the best way to calculate the Cholesky decomposition of $\mathbf{B}$?

Given no additional information the obvious way is to calculate it directly, which incurs a cost of $\mathcal{O}(N^3)$. But suppose we’ve already calculated the lower-triangular Cholesky factor $\mathbf{L} \in \mathbb{R}^{N \times N}$ of $\mathbf{A}$ (i.e., $\mathbf{LL}^\top = \mathbf{A}$). Then, we can use it to calculate the Cholesky decomposition of $\mathbf{B}$ at a reduced cost of $\mathcal{O}(N^2M)$. Here’s how.

Rank-1 Updates

First, let’s consider the simpler case involving just rank-1 updates $$\mathbf{B} \triangleq \mathbf{A} + \mathbf{u} \mathbf{u}^\top,$$ where update factor vector $\mathbf{u} \in \mathbb{R}^{N}$. With some clever manipulations1, the details of which we won’t get into in this post, we can leverage $\mathbf{L}$ to calculate the Cholesky decomposition of $\mathbf{B}$ at a reduced cost of $\mathcal{O}(N^2)$. Such a procedure for rank-1 updates is implemented in the old-school Fortran linear algebra software library LINPACK (but unfortunately not in its successor LAPACK), and also in modern libraries like TensorFlow Probability (TFP).

In TFP, this is implemented in the function named tfp.math.cholesky_update. For example,

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp

update_factor_vector  # Tensor; shape [..., N]
a  # Tensor; shape [..., N, N]

update = tf.linalg.matmul(
    update_factor_vector[..., tf.newaxis],
    update_factor_vector[..., tf.newaxis],

b = a + update  # Tensor; shape [..., N, N]
a_factor = tf.linalg.cholesky(a)  # O(N^3); suppose this is pre-computed and stored

b_factor = tf.linalg.cholesky(b)  # O(N^3), ignores `a_factor`
b_factor_1 = tfp.math.cholesky_update(a_factor, update_factor_vector)  # O(N^2), uses `a_factor`

np.testing.assert_array_almost_equal(b_factor, b_factor_1)

Here cholesky_update takes as arguments chol with shape [B1, ..., Bn, N, N] and u with shape [B1, ..., Bn, N], and returns a lower triangular Cholesky factor of the rank-1 updated matrix chol @ chol.T + u @ u.T in $\mathcal{O}(N^2)$ time.

Low-Rank Updates

Now let’s return to rank-$M$ updates. First let’s write the update factor matrix $\mathbf{U}$ in terms of column vectors $\mathbf{u}_m \in \mathbb{R}^{N}$, $$ \mathbf{U} \triangleq \begin{bmatrix} \mathbf{u}_1 & \cdots & \mathbf{u}_M \end{bmatrix}. $$

Now we can write the rank-$M$ update matrix as a sum of $M$ rank-1 matrices, $$ \mathbf{U} \mathbf{U}^\top = \begin{bmatrix} \mathbf{u}_1 & \cdots & \mathbf{u}_M \end{bmatrix} \begin{bmatrix} \mathbf{u}_1^\top \\ \vdots \\ \mathbf{u}_M^\top \end{bmatrix} = \sum_{m=1}^{M} \mathbf{u}_m \mathbf{u}_m^\top. $$

update_factor_matrix  # Tensor; shape [..., N, M]

# [..., N, 1, M] [..., 1, N, M] -> [..., N, N, M] -> [..., N, N]
update1 = tf.reduce_sum(update_factor_matrix[..., tf.newaxis, :] *
                        update_factor_matrix[..., tf.newaxis, :, :], axis=-1)
# [..., N, M] [..., M, N] -> [..., N, N]
update2 = tf.linalg.matmul(update_factor_matrix,
                           update_factor_matrix, transpose_b=True)

# not exactly equal due to finite precision, but still equal up to high precision
np.testing.assert_array_almost_equal(update1, update2, decimal=14)

Thus seen, a low-rank update is nothing more than a repeated application of rank-1 updates, $$ \begin{align} \mathbf{B} & = \mathbf{A} + \mathbf{U} \mathbf{U}^\top \\ & = \mathbf{A} + \sum_{m=1}^{M} \mathbf{u}_m \mathbf{u}_m^\top \\ & = ((\mathbf{A} + \mathbf{u}_1 \mathbf{u}_1^\top) + \cdots ) + \mathbf{u}_M \mathbf{u}_M^{\top}. \end{align} $$

Therefore, we can simply leverage the $O(N^2)$ procedure for Cholesky decompositions of rank-1 updates and apply it recursively $M$ times to obtain a $O(N^2M)$ procedure for rank-$M$ updates.

Hence, we have:

# [..., N, M] [..., M, N] -> [..., N, N]
update = tf.linalg.matmul(update_factor_matrix,
                          update_factor_matrix, transpose_b=True)
b = a + update  # Tensor; shape [..., N, N]

b_factor = tf.linalg.cholesky(b)  # O(N^3), ignores `a_factor`
b_factor_1 = cholesky_update_iterated(a_factor, update_factor_matrix)  # O(N^2M), uses `a_factor`

np.testing.assert_array_almost_equal(b_factor_1, b_factor)

where function cholesky_update_iterated is implemented as follows:

def cholesky_update_iterated(chol, update_factor_matrix):

    # base case
    if update_factor_matrix.shape[-1] == 0:
        return chol

    prev = cholesky_update_iterated(chol, update_factor_matrix[..., :-1])
    return tfp.math.cholesky_update(prev, update_factor_matrix[..., -1])

We can also implement this iteratively. First we’d use tf.unstack to turn the update factor matrix $\mathbf{U}$ into a list of update factor vectors $\mathbf{u}_m$:

>>> update_factor_vectors = tf.unstack(update_factor_matrix, axis=-1)
>>> assert isinstance(update_factor_vectors, list)  # `update_factor_vectors` is a list
>>> assert len(update_factor_vectors) == M  # ... the list contains M vectors
>>> assert update_factor_vectors[0].shape == (*Bs, N)  # ... and each vector has shape [B1, ..., Bn, N]

Then, we have:

def cholesky_update_iterated(chol, update_factor_matrix):
    new_chol = chol
    for update_factor_vector in tf.unstack(update_factor_matrix, axis=-1):
        new_chol = tfp.math.cholesky_update(new_chol, update_factor_vector)
    return new_chol

The astute reader will recognize that this is simply an special case of the itertools.accumulate or functools.reduce patterns, where the binary operator is tfp.math.cholesky_update, the iterable is tf.unstack(update_factor, axis=-1) and the initial value is chol.

Therefore, we can also implement it neatly using the one-liner:

from functools import reduce

def cholesky_update_iterated(chol, update_factor_matrix):
    return reduce(tfp.math.cholesky_update, tf.unstack(update_factor_matrix, axis=-1), chol)


In summary, we showed that to efficiently calculate the Cholesky decomposition of a matrix perturbed by a low-rank update, one just needs to iteratively calculate that of the same matrix perturbed by a series of rank-1 updates. Better yet, all of this can be done with a simple one-liner!

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  1. Seeger, M. (2004). Low rank updates for the Cholesky decomposition. ↩︎

Louis Tiao
Louis Tiao
Machine Learning Research Scientist (PhD Candidate)

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