https://github.com/infiniteopt/infiniteopt.jl
An intuitive modeling interface for infinite-dimensional optimization problems.
https://github.com/infiniteopt/infiniteopt.jl
differential-equations dynamic-programming julia measure-theory modeling-language nonlinear-optimization optimal-control optimization pde-constrained-optimization stochastic-optimization
Last synced: 19 days ago
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An intuitive modeling interface for infinite-dimensional optimization problems.
- Host: GitHub
- URL: https://github.com/infiniteopt/infiniteopt.jl
- Owner: infiniteopt
- License: mit
- Created: 2019-08-13T14:27:07.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2026-05-19T14:25:16.000Z (26 days ago)
- Last Synced: 2026-05-19T15:11:13.143Z (26 days ago)
- Topics: differential-equations, dynamic-programming, julia, measure-theory, modeling-language, nonlinear-optimization, optimal-control, optimization, pde-constrained-optimization, stochastic-optimization
- Language: Julia
- Homepage: https://infiniteopt.github.io/InfiniteOpt.jl/stable
- Size: 14.3 MB
- Stars: 318
- Watchers: 8
- Forks: 24
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
---
A `JuMP` extension for intuitively modeling general infinite-dimensional optimization
problems and efficiently solving them. Such areas include [stochastic programming](https://en.wikipedia.org/wiki/Stochastic_programming),
[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming),
space-time optimization, and more. `InfiniteOpt` serves as an **easy-to-use modeling
interface** for these advanced problem types that can be used by those with little
to no background in these areas. It also it contains a wealth of capabilities
making it a powerful and convenient tool for advanced users. Notably, it can
**significantly reduce solution times** via highly efficient CPU and GPU solution methods
with its [InfiniteExaModels](https://github.com/infiniteopt/InfiniteExaModels.jl) backend.
**Current Version:** [](https://juliahub.com/ui/Packages/InfiniteOpt/p3GvY) [](https://juliahub.com/ui/Packages/InfiniteOpt/p3GvY)
| **Documentation** | **Build Status** | **Citation** |
|:-------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:--------------------------------------:|
| [](https://infiniteopt.github.io/InfiniteOpt.jl/stable) | [](https://github.com/infiniteopt/InfiniteOpt.jl/actions/workflows/ci.yml) [](https://codecov.io/github/infiniteopt/InfiniteOpt.jl?branch=release-0.6) | [](https://doi.org/10.1016/j.compchemeng.2021.107567) |
| [](https://infiniteopt.github.io/InfiniteOpt.jl/dev) | [](https://github.com/infiniteopt/InfiniteOpt.jl/actions/workflows/ci.yml) [](https://codecov.io/github/infiniteopt/InfiniteOpt.jl?branch=master) | |
It builds upon `JuMP` to add support for many complex modeling objects which
include:
- Infinite parameters (e.g., time, space, uncertainty, etc.)
- Finite parameters (similar to `ParameterJuMP`)
- Infinite variables (decision functions) (e.g., `y(t, x)`)
- Derivatives (e.g., `∂y(t, x)/∂t`)
- Measures (e.g., `∫y(t,x)dt`, `𝔼[y(ξ)]`)
The unifying modeling abstraction behind `InfiniteOpt` captures a wide spectrum
of disciplines which include dynamic, PDE, stochastic, and semi-infinite
optimization. Moreover, we facilitate transferring techniques between these
to synthesize new optimization paradigms!
Comments, suggestions and improvements are welcome and appreciated.
## License
`InfiniteOpt` is licensed under the [MIT "Expat" license](./LICENSE).
## Installation
`InfiniteOpt.jl` is a registered [Julia](https://julialang.org/) package and
can be installed by entering the following in the REPL.
```julia
julia> ]
(v1.12) pkg> add InfiniteOpt
```
## Documentation
[](https://infiniteopt.github.io/InfiniteOpt.jl/stable)
Please visit our [documentation pages](https://infiniteopt.github.io/InfiniteOpt.jl/stable)
to learn more. These pages are quite extensive and feature overviews, guides,
manuals, tutorials, examples, and more!
## Questions
For additional help please visit and post in our
[discussion forum](https://github.com/infiniteopt/InfiniteOpt.jl/discussions).
## Citing
[](https://doi.org/10.1016/j.compchemeng.2021.107567)
[](https://arxiv.org/abs/2106.12689)
If you use InfiniteOpt.jl in your research, we would greatly appreciate your
citing it.
```latex
@article{pulsipher2022unifying,
title = {A unifying modeling abstraction for infinite-dimensional optimization},
journal = {Computers & Chemical Engineering},
volume = {156},
year = {2022},
issn = {0098-1354},
doi = {https://doi.org/10.1016/j.compchemeng.2021.107567},
url = {https://www.sciencedirect.com/science/article/pii/S0098135421003458},
author = {Joshua L. Pulsipher and Weiqi Zhang and Tyler J. Hongisto and Victor M. Zavala},
}
```
A pre-print version is freely available though [arXiv](https://arxiv.org/abs/2106.12689).
## Project Status
The package is tested against Julia's LTS release and the latest released version on Linux, Mac, and Windows.
## Contributing
`InfiniteOpt` is being actively developed and suggestions or other forms of contribution are encouraged.
There are many ways to contribute to this package. For more information please
visit [CONTRIBUTING](https://github.com/infiniteopt/InfiniteOpt.jl/blob/master/CONTRIBUTING.md).