{"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/gpu-accelerated-security-constrained-optimal","title":"GPU Accelerated Security Constrained Optimal Power Flow","arxiv_id":"2410.17203","date":"2024-10-22","proceeding":null,"authors":["Anthony Degleris","Abbas El Gamal","Ram Rajagopal"],"abstract":"We propose a GPU accelerated proximal message passing algorithm for solving contingency-constrained DC optimal power flow problems (OPF). We consider a highly general formulation of OPF that uses a sparse device-node model and supports a broad range of devices and constraints, e.g., energy storage and ramping limits. Our algorithm is a variant of the alternating direction method multipliers (ADMM) that does not require solving any linear systems and only consists of sparse incidence matrix multiplies and vectorized scalar operations. We develop a pure PyTorch implementation of our algorithm that runs entirely on the GPU. The implementation is also end-to-end differentiable, i.e., all updates are automatic differentiation compatible. We demonstrate the performance of our method using test cases of varying network sizes and time horizons. Relative to a CPU-based commercial optimizer, our implementation achieves well over 100x speedups on large test cases, solving problems with over 500 million variables in under a minute on a single GPU.","url_abs":"https://arxiv.org/abs/2410.17203v1","url_pdf":"https://arxiv.org/pdf/2410.17203v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"gpu-accelerated-security-constrained-optimal","repo_url":"https://github.com/degleris1/zap","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}