deps(deps): bump lineax from 0.1.0 to 0.1.1

[dependabot]

Bumps lineax from 0.1.0 to 0.1.1.

Release notes

Sourced from lineax's releases.

lineax v0.1.1

This is mostly a bug fix, documentation and under-the-hood performance improvement release with one new feature—the lx.invert transformation which produces an operator representing the inverse of a matrix. Use of coloring rules should make using implicit solvers in diffrax for tridiagonal Jacobian/FunctionLinearOperators at least an order of magnitude faster.

Breaking Changes

• Extraction of diagonal/tridiagonals of now leverages the promise of a matrix being tagged as diagonal/tridiagonal more heavily. If you have previously used the tag for an operator that you just wanted lineax to TREAT as diagonal/tridiagonal you may now get incorrect results. In most cases the right fix probably be to first manually extract (tri)diagonal and construct the (Tri)DiagonalOperator explicitly, please raise an issue if you need any further assistance.
• lineax.linear_solve now stop-gradient's automatically (#213), it is unlikely this will break any existing use-cases but may make manual stop-gradienting unecessary
• Removed AuxLinearOperator (#203)

Features

• Add invert helper function to wrap lineax.linear_solve in FunctionLinearOperator. Materialising an inverse is now as simple as lx.invert(op).as_matrix(). (#206)

Compatibility

• lineax v0.1.1 now requires JAX >= 0.10.0 which provides a lowering to LAPACK/cuSolver's` ormqr for more efficient QR solve adopted in #219.

Bugfixes

• Fix derived tag check rules for composite operators (e.g. Composed/Neg/Mul/AddLinearOperator) (#192)
• Linearisation of functions custom_vjp's are now supported by lineax.linearize(JacobianLinearOperator(f, x, jac="bwd")) by using jax.linear_transpose under the hood. (#191)
• Complex positive/negative semi-definite matrices no longer register as symmetric (#200)
• lineax.LSMR no longer fails when initial residual is exactly zero. (HUGE thanks to @​f0uriest for spotting this tricky and hard-to-spot bug #202)
• Differentiating through linear_solve's no longer differentiates through solver.init this means using solver's with no or incorrect jvp rule is now possible (#212)

Performance

• Coloring rules now used to massively speed up diagonal/tridiagonal extraction of tagged Jacobian/FunctionLinearOperators (#164, #165)
• Normal and iterative solvers now apply lineax.linearise under the hood to avoid multiple sequential AD passes (#198)
• Furthermore, lineax.Normal(lineax.Cholesky()) now materialises the inner operator before constructing the Gram matrix (#207)
• ComposedLinearOperator.as_matrix no longer materialises each matrix first but instead batches mv of the first operator over the second matrix (#196)
• JAX's ormqr now used for more efficient QR solves (#219)

Documentation

• The lineax.LSMR iterative least square solver is now properly documented (#204) after @​f0uriest's #202 bug-fixes make it more robust.

Other repo infra PR's not affecting Python package include #214, #216 and #218.

New Contributors

• @​patrick-kidger-bot 🤖 made their first contribution in patrick-kidger/lineax#216

Full Changelog: patrick-kidger/lineax@v0.1.0...v0.1.1

Commits

• 2bf9824 0.1.1 release (#220)
• 53edb88 Use ormqr for more efficient QR solve (#219)
• 07b477b added LSMR to docs (#204)
• 0cb6090 Add invert helper function to wrap linear_solve in FunctionLinearOperator (#206)
• 164a74f Streamline tridiagonalisation of JacobianLinearOperator/FunctionLinearOperato...
• 886ea57 Efficient diagonal extraction when operator is promised to be diagonal
• eb8cb0d Use dependency groups, uv, and local pre-commit hooks
• 83cf26f bump pymdown-extensions; pin pygments
• b6f3087 prevent linear_solve calling jvp of solver.init
• 5e00f8c Fix construct_singular_matrix not respecting tags
• Additional commits viewable in compare view

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