added LSMR to docs#204
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Happy with the wording and structure here, but I think the h3 heading doesn't work as well as the original info box because the current theme doesn't distinguish much between h2 and h3 and its easy to get confused what constitutes a new section. However, I do like that your restructure puts the multi-class solvers in the toc. What do you think about try h4 instead? H3 
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No strong feelings! Happy with whatever you think is most readable (h3, h4, admonition, ...) |
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May 1, 2026
This is mostly a bug fix, documentation and under-the-hood performance improvement release with one new feature—the `lx.invert` [transformation](https://docs.kidger.site/lineax/api/linear_solve/#invert) which produces an operator representing the inverse of a matrix. Use of coloring rules should make using implicit solvers in [diffrax](https://docs.kidger.site/diffrax/) for tridiagonal `Jacobian/FunctionLinearOperator`s 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 will probably be to first manually extract (tri)diagonal and construct the `(Tri)DiagonalOperator` explicitly, please raise an [issue](https://github.com/patrick-kidger/lineax/issues/new) 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](https://docs.jax.dev/en/latest/_autosummary/jax.lax.linalg.ormqr.html) 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 #216 **Full Changelog**: v0.1.0...v0.1.1
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Fixes #193.
(Also I reordered a bit of this documentation whilst I was here)