matfree 0.0.14

Matrix-free numerical linear algebra.

matfree: Matrix-free linear algebra in JAX

Randomised and deterministic matrix-free methods for trace estimation, matrix functions, and/or matrix factorisations. Builds on JAX.

• ⚡ Stochastic trace estimation including batching, control variates, and uncertainty quantification
• ⚡ A stand-alone implementation of stochastic Lanczos quadrature
• ⚡ Matrix-decomposition algorithms for large sparse eigenvalue problems
• ⚡ Polynomial methods for approximating functions of large matrices

and many other things. Everything is natively compatible with the rest of JAX: JIT compilation, automatic differentiation, vectorisation, and PyTrees. Let us know what you think about matfree!

Installation

To install the package, run

pip install matfree

Important: This assumes you already have a working installation of JAX. To install JAX, follow these instructions. To combine matfree with a CPU version of JAX, run

pip install matfree[cpu]

which is equivalent to combining pip install jax[cpu] with pip install matfree. (But do not only use matfree on CPU!)

Minimal example

Import matfree and JAX, and set up a test problem.

>>> import jax
>>> import jax.numpy as jnp
>>> from matfree import hutchinson
>>>
>>> jnp.set_printoptions(1)

>>> A = jnp.reshape(jnp.arange(12.0), (6, 2))
>>>
>>> def matvec(x):
...     return A.T @ (A @ x)
...

Estimate the trace of the matrix:

>>> # Determine the shape of the base-samples
>>> input_like = jnp.zeros((2,), dtype=float)
>>> sampler = hutchinson.sampler_rademacher(input_like, num=10_000)
>>>
>>> # Set Hutchinson's method up to compute the traces
>>> # (instead of, e.g., diagonals)
>>> integrand = hutchinson.integrand_trace(matvec)
>>>
>>> # Compute an estimator
>>> estimate = hutchinson.hutchinson(integrand, sampler)

>>> # Estimate
>>> key = jax.random.PRNGKey(1)
>>> trace = jax.jit(estimate)(key)
>>>
>>> print(trace)
508.9
>>>
>>> # for comparison:
>>> print((jnp.trace(A.T @ A)))
506.0

Tutorials

Find many more tutorials in Matfree's documentation.

These tutorials include, among other things:

• Log-determinants: Use stochastic Lanczos quadrature to compute matrix functions.
• Pytree-valued states: Combining neural-network Jacobians with stochastic Lanczos quadrature.
• Control variates: Use control variates and multilevel schemes to reduce variances.
• Higher moments and UQ: Compute means, variances, and other moments simultaneously.
• Vector calculus: Use matrix-free linear algebra to implement vector calculus.

Let us know what you use matfree for!

Use Matfree's continuous integration

To install all test-related dependencies, (assuming JAX is installed; if not, run pip install .[cpu]), execute

pip install .[test]

Then, run the tests via

make test

Install all formatting-related dependencies via

pip install .[format]

and format the code via

make format

To lint the code, install the pre-commit hook

pip install .[lint]
pre-commit install

and run the linters via

make lint

Install the documentation-related dependencies as

pip install .[doc]

Preview the documentation via

make doc-preview

and check whether the docs build correctly via

make doc-build

Contribute to Matfree

Contributions are absolutely welcome!

Issues:

Most contributions start with an issue. Please don't hesitate to create issues in which you ask for features, give feedback on performances, or simply want to reach out.

Pull requests:

To make a pull request, proceed as follows:

• Fork the repository.
• Install all dependencies with pip install .[full] or pip install -e .[full].
• Make your changes.
• From the root of the project, run the tests via make test, and check out make format and make lint as well. Use the pre-commit hook if you like.

When making a pull request, keep in mind the following (rough) guidelines:

• Most PRs resolve an issue.
• Most PRs contain a single commit. Here is how we can write better commit messages.
• Most enhancements (e.g. new features) are covered by tests.

Extend Matfree's documentation

Write a new tutorial:

To add a new tutorial, create a Python file in tutorials/ and fill it with content. Use docstrings (mirror the style in the existing tutorials). Make sure to satisfy the formatter and linter. That's all.

Then, the documentation pipeline will automatically convert those into a format compatible with Jupytext, which subsequently includes it into the documentation. If you do not want to make the tutorial part of the documentation, make the filename have a leading underscore.

Extend the developer documentation:

To extend the developer documentation, create a new section in the README. Use a second-level header (a header that starts with "##") and fill the section with content. Then, the documentation pipeline will turn this section into a page in the developer documentation.

Create a new module:

To make a new module appear in the documentation, create the new module in matfree/, and fill it with content. Unless the module name starts with an underscore or is placed in the backend, the documentation pipeline will take care of the rest.