{"id":13520950,"url":"https://github.com/patrick-kidger/optimistix","last_synced_at":"2025-11-17T15:31:56.686Z","repository":{"id":198719652,"uuid":"699980700","full_name":"patrick-kidger/optimistix","owner":"patrick-kidger","description":"Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/","archived":false,"fork":false,"pushed_at":"2025-04-21T14:21:07.000Z","size":884,"stargazers_count":408,"open_issues_count":45,"forks_count":21,"subscribers_count":8,"default_branch":"main","last_synced_at":"2025-04-21T15:24:40.806Z","etag":null,"topics":["deep-learning","equinox","jax","neural-networks","optimisation","optimization"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/patrick-kidger.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-10-03T17:59:45.000Z","updated_at":"2025-04-20T12:46:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"433e0ecb-fe67-495a-b107-ff23e2fd1143","html_url":"https://github.com/patrick-kidger/optimistix","commit_stats":{"total_commits":216,"total_committers":8,"mean_commits":27.0,"dds":"0.36111111111111116","last_synced_commit":"a8b77872bcd77a7f9859b1b2c09928b39636aa8b"},"previous_names":["patrick-kidger/optimistix"],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrick-kidger%2Foptimistix","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrick-kidger%2Foptimistix/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrick-kidger%2Foptimistix/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/patrick-kidger%2Foptimistix/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/patrick-kidger","download_url":"https://codeload.github.com/patrick-kidger/optimistix/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254219374,"owners_count":22034397,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","equinox","jax","neural-networks","optimisation","optimization"],"created_at":"2024-08-01T06:00:24.920Z","updated_at":"2025-11-17T15:31:51.659Z","avatar_url":"https://github.com/patrick-kidger.png","language":"Python","funding_links":[],"categories":["Models and Projects","Recently Updated","Python","Libraries"],"sub_categories":["[Feb 16, 2025](/content/2025/02/16/README.md)","New Libraries"],"readme":"\u003ch1 align='center'\u003eOptimistix\u003c/h1\u003e\n\nOptimistix is a [JAX](https://github.com/google/jax) library for nonlinear solvers: root finding, minimisation, fixed points, and least squares.\n\nFeatures include:\n\n- interoperable solvers: e.g. autoconvert root find problems to least squares problems, then solve using a minimisation algorithm.\n- modular optimisers: e.g. use a BFGS quadratic bowl with a dogleg descent path with a trust region update.\n- using a PyTree as the state.\n- fast compilation and runtimes.\n- interoperability with [Optax](https://github.com/deepmind/optax).\n- all the benefits of working with JAX: autodiff, autoparallelism, GPU/TPU support etc.\n\n## Installation\n\n```bash\npip install optimistix\n```\n\nRequires Python 3.10+ and JAX 0.4.38+ and [Equinox](https://github.com/patrick-kidger/equinox) 0.11.11+.\n\n## Documentation\n\nAvailable at [https://docs.kidger.site/optimistix](https://docs.kidger.site/optimistix).\n\n## Quick example\n\n```python\nimport jax.numpy as jnp\nimport optimistix as optx\n\n# Let's solve the ODE dy/dt=tanh(y(t)) with the implicit Euler method.\n# We need to find y1 s.t. y1 = y0 + tanh(y1)dt.\n\ny0 = jnp.array(1.)\ndt = jnp.array(0.1)\n\ndef fn(y, args):\n    return y0 + jnp.tanh(y) * dt\n\nsolver = optx.Newton(rtol=1e-5, atol=1e-5)\nsol = optx.fixed_point(fn, solver, y0)\ny1 = sol.value  # satisfies y1 == fn(y1)\n```\n\n## Citation\n\nIf you found this library to be useful in academic work, then please cite: ([arXiv link](https://arxiv.org/abs/2402.09983))\n\n```bibtex\n@article{optimistix2024,\n    title={Optimistix: modular optimisation in JAX and Equinox},\n    author={Jason Rader and Terry Lyons and Patrick Kidger},\n    journal={arXiv:2402.09983},\n    year={2024},\n}\n```\n\n## See also: other libraries in the JAX ecosystem\n\n**Always useful**  \n[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!  \n[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.  \n\n**Deep learning**  \n[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.  \n[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).  \n[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).  \n[paramax](https://github.com/danielward27/paramax): parameterizations and constraints for PyTrees.  \n\n**Scientific computing**  \n[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.  \n[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.  \n[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.  \n[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy\u003c-\u003eJAX conversion; train symbolic expressions via gradient descent.  \n[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)  \n\n**Awesome JAX**  \n[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.  \n\n## Credit\n\nOptimistix was primarily built by Jason Rader (@packquickly): [Twitter](https://twitter.com/packquickly); [GitHub](https://github.com/packquickly); [Website](https://www.packquickly.com/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatrick-kidger%2Foptimistix","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpatrick-kidger%2Foptimistix","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatrick-kidger%2Foptimistix/lists"}