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Python proof of concept for BCHOL. Solves for x in Ax = b, using the recursive Schur Linear Quadratic Regulator. It requires A to be a positive semi-definite matrix to guarantee good result.

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BCHOL-python

Python proof of concept for BCHOL.

Solves for x in Ax = b, using the Recursive Schur Linear Quadratic Regulator explained in the paper A Parallell Linear System Solver for Optimal Control by Brian E.Jackson. It requires A to be a positive semi-definite matrix to guarantee a good result.

Requirements

  • Python 3.10+

  • The following libraries (Numpy, Scipy) are included in the requirments.txt and can be downloaded with the following command

pip3 install -r requirements.txt

Usage

If you already have a defined LQR problem in a KKT form in a saved file .json/.csv you can look at solve_load.py for an example.

If you just have an A matrix and a b vector look at solve_build.py for an example.

Both files will return an xyz solution vector.

Citing

Author: Yana Botvinnik Contact: [email protected]

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Python proof of concept for BCHOL. Solves for x in Ax = b, using the recursive Schur Linear Quadratic Regulator. It requires A to be a positive semi-definite matrix to guarantee good result.

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