https://github.com/datamole-ai/gomez
Framework and implementation for mathematical optimization and solving non-linear systems of equations.
https://github.com/datamole-ai/gomez
equation-solver mathematics numerical-methods optimization
Last synced: 3 months ago
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Framework and implementation for mathematical optimization and solving non-linear systems of equations.
- Host: GitHub
- URL: https://github.com/datamole-ai/gomez
- Owner: datamole-ai
- License: mit
- Created: 2021-12-20T12:04:34.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-08-23T13:14:04.000Z (10 months ago)
- Last Synced: 2025-10-27T14:56:16.052Z (8 months ago)
- Topics: equation-solver, mathematics, numerical-methods, optimization
- Language: Rust
- Homepage:
- Size: 226 KB
- Stars: 50
- Watchers: 2
- Forks: 5
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# gomez
[](https://github.com/datamole-ai/gomez/actions)
[](https://github.com/datamole-ai/gomez/blob/main/LICENSE)
[](https://crates.io/crates/gomez)
[](https://docs.rs/gomez)
_gomez_ is a framework and implementation for **mathematical optimization** and
solving **non-linear systems of equations**.
The library is written completely in Rust. Its focus is on being useful for
**practical problems** and having API that is simple for easy cases as well as
flexible for complicated ones. The name stands for ***g***lobal
***o***ptimization & ***n***on-linear ***e***quations ***s***olving, with a few
typos.
## Practical problems
The main goal is to be useful for practical problems. This is manifested by the
following features:
* _Derivative-free_. No algorithm requires an analytical derivative (gradient,
Hessian, Jacobian). Methods that use derivatives approximate it using finite
difference method1.
* _Constraints_ support. It is possible to specify the problem domain with
constraints2, which is necessary for many engineering applications.
* Non-naive implementations. The code is not a direct translation of a textbook
pseudocode. It's written with performance in mind and applies important
techniques from numerical mathematics. It also tries to handle situations that
hurt the methods but occur in practice.
1 There is a plan to provide ways to override this approximation with
a real derivative.
2 Currently, only unconstrained and box-bounded domains are
supported.
## Algorithms
* [Trust region](algo::trust_region) – Recommended method to be used as a
default and it will just work in most cases.
* [LIPO](algo::lipo) – Global optimization algorithm useful for searching good
initial guesses in combination with a numerical algorithm.
* [Steffensen](algo::steffensen) – Fast and lightweight method for solving
one-dimensional systems of equations.
* [Nelder-Mead](algo::nelder_mead) – Direct search optimization method that does
not use any derivatives.
This list will be extended in the future. But at the same time, having as many
algorithms as possible is _not_ the goal. Instead, the focus is on providing
quality implementations of battle-tested methods.
## Roadmap
Listed *not* in priority order.
* [Homotopy continuation
method](http://homepages.math.uic.edu/~jan/srvart/node4.html) to compare the
performance with the trust region method
* Conjugate gradient method
* Experimentation with various global optimization techniques for initial guess
search
* Evolutionary/nature-inspired algorithms
* Bayesian optimization
* Focus on initial guesses search and tools for analysis in general
## License
Licensed under [MIT](LICENSE).
There are `gsl-wrapper` and `gsl-sys` crates which are licensed under the
[GPLv3](http://www.gnu.org/licenses/gpl-3.0.html) identically as
[GSL](https://www.gnu.org/software/gsl/) itself. This code is part of the
repository but is not part of the gomez library. Its purpose is solely for
comparison in the benchmarks.