solvers package#

In tinygp, “solvers” provide a swappable low-level interface for implementing the linear algebra required to execute Gaussian Process models. At the moment, tinygp includes two solvers, but new solvers can be implemented as external packages or as pull requests to the main tinygp GitHub project. The two built in solvers are:

  1. DirectSolver: A solver that uses a naive approach to solving the required linear systems. This can still be pretty fast if you have access to a GPU that can run jax code. This is the default solver, and it can be used with any kernel implemented by tinygp. Up to numerical precision, this is an exact solver.

  2. QuasisepSolver: An experiemental scalable solver that exploits the “quasiseparable” structure found in many GP covariance matrices to make the required linear algabra possible in linear scaling with the size of the dataset. These methods were previously implemented as part of the celerite project.

Users generally won’t instantiate these solvers directly and tinygp should generally be able to figure out the best one to use. But you can use a specific solver using the solver argument to tinygp.GaussianProcess as follows:

gp = tinygp.GaussianProcess(..., solver=tinygp.solvers.DirectSolver)

The details for the included solvers are given below, but this is a pretty low-level feature and the details are definitely subject to change!

DirectSolver(X, variance_value, ...)

A direct solver that uses jax's built in Cholesky factorization

QuasisepSolver(X, matrix, factor)

A scalable solver that uses quasiseparable matrices