A JuMP extension for expressing and solving infinite-dimensional optimization problems. Such areas include stochastic programming, 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.

⚠️ v0.4 introduced breaking changes: See the documentation for details.

Documentation	Build Status	Citation

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 (e.g., y(t, x))
• Derivatives (e.g., ∂y(t, x)/∂t)
• Measures (e.g., ∫y(t,x)dt, 𝔼[y(ξ)])
• More

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.

InfiniteOpt is licensed under the MIT "Expat" license.

InfiniteOpt.jl is a registered package and can be installed by entering the following in the package manager.

(v1.6) pkg> add InfiniteOpt

Please visit our documentation pages to learn more. These pages are quite extensive and feature overviews, guides, manuals, tutorials, examples, and more!

If you use InfiniteOpt.jl in your research, we would greatly appreciate your citing it.

@misc{pulsipher2021unifying,
      title={A Unifying Modeling Abstraction for Infinite-Dimensional Optimization}, 
      author={Joshua L. Pulsipher and Weiqi Zhang and Tyler J. Hongisto and Victor M. Zavala},
      year={2021},
      eprint={2106.12689},
      archivePrefix={arXiv},
      primaryClass={math.OC}
}

The package is tested against Julia 1.0 and 1.6 on Linux and Windows.

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.