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While OBBT can yield near-global solutions via tight convex relaxations, its runtime remains a critical bottleneck on large-scale power grids. Our key contribution is a\n                    <jats:italic>dynamic policy<\/jats:italic>\n                    that selects smaller subsets of voltage magnitude and phase-angle difference variables for sequential bound tightening at every iteration of the OBBT algorithm. This ensures that the bound-tightening process remains adaptive, thereby circumventing the stalling in the optimality gap often observed with static, predetermined subsets (like in our previous work (Cengil in Electric Power Syst Res 212: 108275, 2022)). By leveraging historical load profiles to re-evaluate and rank variables dynamically, our proposed framework preserves the benefits of OBBT while significantly reducing computation time. Through a parallel implementation of the proposed OBBT algorithm, we observe\n                    <jats:italic>an average speed-up of 9.3<\/jats:italic>\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times $$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:italic>with maximum improvement up to 20<\/jats:italic>\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times $$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    \u2013 relative to the conventional exhaustive OBBT \u2013 on a held-out set of benchmark instances that range in size up to 3,375 buses. To the best of our knowledge, this is the first ML-based OBBT approach to demonstrate such large-scale performance gains on realistic AC-OPF problems, offering a promising pathway toward more efficient global solutions in power system operations.\n                  <\/jats:p>","DOI":"10.1007\/s10589-025-00715-7","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T18:41:04Z","timestamp":1755110464000},"page":"761-786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem"],"prefix":"10.1007","volume":"92","author":[{"given":"Fatih","family":"Cengil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harsha","family":"Nagarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russell","family":"Bent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Eksioglu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2999-6783","authenticated-orcid":false,"given":"Burak","family":"Eksioglu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"715_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108275","volume":"212","author":"F Cengil","year":"2022","unstructured":"Cengil, F., Nagarajan, H., Bent, R., Eksioglu, S., Eksioglu, B.: Learning to accelerate globally optimal solutions to the AC optimal power flow problem. 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