ML Community Day is November 9! Join us for updates from TensorFlow, JAX, and more Learn more

tfp.experimental.linalg.simple_robustified_cholesky

Use no_pivot_ldl to robustify a Cholesky factorization.

Given a symmetric matrix A, this function attempts to give a factorization A + E = LL^T where L is lower triangular, LL^T is positive definite, and E is small in some suitable sense. This is useful for nearly positive definite symmetric matrices that are otherwise numerically difficult to Cholesky factor.

The algorithm proceeds as follows. The input is factored A = LDL^T, and the too-small diagonal entries of D are increased to the tolerance. Then L @ sqrt(D) is returned.

This algorithm is similar in spirit to a true modified Cholesky factorization ([1], [2]). However, it does not use pivoting or other strategies to ensure stability, so may not work well for e.g. ill-conditioned matrices. Generally speaking, a modified Cholesky factorization of a symmetric matrix A is a factorization P(A+E)P^T = LDL^T, where P is a permutation matrix, L is unit lower triangular, and D is (block) diagonal and positive definite. Ideally such an algorithm would ensure the following:

  1. If A is sufficiently positive definite, E is zero.

  2. If F is the smallest matrix (in Frobenius norm) such that A + F is positive definite, then E is not much larger than F.

  3. A + E is reasonably well-conditioned.

  4. It is not too much more expensive than the usual Cholesky factorization.

The references give more sophisticated algorithms to ensure all of the above. In the simple case where A = LDL^T does not require pivoting, simple_robustified_cholesky will agree and satisfy the above criteria. However, in general it may fail to be stable or satisfy 2 and 3.

References

[1]: Nicholas Higham. What is a modified Cholesky factorization? https://nhigham.com/2020/12/22/what-is-a-modified-cholesky-factorization/

[2]: Sheung Hun Cheng and Nicholas Higham, A Modified Cholesky Algorithm Based on a Symmetric Indefinite Factorization, SIAM J. Matrix Anal. Appl. 19(4), 1097–1110, 1998.

matrix A batch of symmetric square matrices, with shape [..., n, n].
tol Minimum for the diagonal. Default: 1e-6.
name Python str name prefixed to Ops created by this function. Default value: 'simple_robustified_cholesky'.

triangular_factor The lower triangular Cholesky factor, modified as above. This will have shape [..., n, n]. Callers should check for nans or other evidence of instability.