{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/protect-parallelized-construction-of-safety","title":"PRoTECT: Parallelized Construction of Safety Barrier Certificates for Nonlinear Polynomial Systems","arxiv_id":"2404.14804","date":"2024-04-23","proceeding":null,"authors":["Ben Wooding","Viacheslav Horbanov","Abolfazl Lavaei"],"abstract":"We develop an open-source software tool, called PRoTECT, for the parallelized construction of safety barrier certificates (BCs) for nonlinear polynomial systems. This tool employs sum-of-squares (SOS) optimization programs to systematically search for polynomial-type BCs, while aiming to verify safety properties over four classes of dynamical systems: (i) discrete-time stochastic systems, (ii) discrete-time deterministic systems, (iii) continuous-time stochastic systems, and (iv) continuous-time deterministic systems. PRoTECT is implemented in Python as an application programming interface (API), offering users the flexibility to interact either through its user-friendly graphic user interface (GUI) or via function calls from other Python programs. PRoTECT leverages parallelism across different barrier degrees to efficiently search for a feasible BC.","url_abs":"https://arxiv.org/abs/2404.14804v2","url_pdf":"https://arxiv.org/pdf/2404.14804v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"protect-parallelized-construction-of-safety","repo_url":"https://github.com/kiguli/protect","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}