Source code to derive graph properties of contextuality from linear programs

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1638832666100&rft.title=Source code to derive graph properties of contextuality from linear programs&rft.identifier=10.25912/RDF_1638832666100&rft.publisher=Queensland University of Technology&rft.description=Prior to reading, this analysis assumes you are familiar with the following works: [1] Acin, A., Fritz, T., Leverrier, A., and Sainz, A. B. (2015). A combinatorial approach to nonlocality and contextuality. Communications in Mathematical Physics, 334(2):533–628. [2]  (2021) Modelling contextuality amidst causal influences by means of a computationally tractable combinatorial approach. PhD thesis, Queensland University of Technology. This iPython Jupyter Notebook serves as an interactive and clarified method through which one can understand the process used to derive the novel graph properties of [2] which characterise contextuality. For this example, I'll be deriving the graph properties for a Bell-CHSH scenario. &rft.creator=Obeid, Abdul Karim &rft.date=2021&rft.edition=1&rft.relation=https://eprints.qut.edu.au/226105/&rft_rights=© Abdul Karim Obeid, 2021. &rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/4.0/&rft_subject=Quantum information, computation and communication&rft_subject=Quantum physics&rft_subject=PHYSICAL SCIENCES&rft.type=dataset&rft.language=English Access the data