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Kona is a library for nonlinear (equality) constrained optimization. The stable master branch currently provides several gradient-based optimization algorithms for both constrained and unconstrained problems. Development is ongoing to support inequality constrained problems at a future date.
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README.md

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Kona - A Parallel Optimization Framework

Install Guide

If you already have a Python distribution, Kona can be installed using pip install -e . --user run from the root directory of the repo.

On macOS, we strongly recommend installing Miniconda first before installing Kona on top. Using the system Python distribution on macOS can break the operating system.

For a quick start guide on how to run optimization problems with Kona, or for more detail on macOS installations, please refer to the documentation.

What is it?

Kona is a library for non-convex and nonlinear (equality) constrained optimization. The master branch currently provides several gradient-based optimization algorithms for both constrained and unconstrained problems. Development is ongoing to support inequality constrained problems at a future date.

Who is it for?

Kona was designed primarily for large-scale partial-differential-equation (PDE) governed optimization problems; however it is suitable for any (sufficiently smooth) problem where the objective function and/or constraints require the solution of a computational expensive state equation.

As a consequence of its abstracted vector and matrix implementations, Kona is also useful for developing new optimization algorithms for PDE-governed optimization.

Please refer to the API reference and use examples in the documentation for more details on Kona's parallel-agnostic implementation and linear algebra abstraction.

Who is developing it?

Kona was originally written by Dr. Jason E. Hicken in C++. This old version is now deprecated, but still available on BitBucket.

The current Python implementation of Kona is being developed and maintained by the Optimal Design Lab research group at Rensselaer Polytechnic Institute.

Acknowledgements

This work is supported by the National Science Foundation under Grant No. 1332819, and the National Aeronautics and Space Administration under Grant No. NNX14AC73A.

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