[2403.20000] Computational Complexity of the Recoverable Robust Shortest Path Problem with Discrete Recourse Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computational Complexity arXiv:2403.20000 (cs) [Submitted on 29 Mar 2024 ( v1 ), last revised 3 Jul 2026 (this version, v3)] Title: Computational Complexity of the Recoverable Robust Shortest Path Problem with Discrete Recourse Authors: Marcel Jackiewicz , Adam Kasperski , Paweł Zieliński View a PDF of the paper titled Computational Complexity of the Recoverable Robust Shortest Path Problem with Discrete Recourse, by Marcel Jackiewicz and 2 other authors View PDF HTML (experimental) Abstract: In this paper the recoverable robust shortest path problem is investigated. Discrete budgeted interval uncertainty representation is used to model uncertain second-stage arc costs. The known complexity results for this problem are strengthened. It is shown that it is Sigma_3^p-hard for the arc exclusion and the arc symmetric difference neighborhoods. Furthermore, it is also proven that the inner adversarial problem for these neighborhoods is Pi_2^p-hard. Subjects: Computational Complexity (cs.CC) Cite as: arXiv:2403.20000 [cs.CC] (or arXiv:2403.20000v3 [cs.CC] for this version) https://doi.org/10.48550/arXiv.2403.20000 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Adam Kasperski [ view email ] [v1] Fri, 29 Mar 2024 06:27:44 UTC (149 KB) [v2] Thu, 30 Apr 2026 08:35:03 UTC (110 KB) [v3] Fri, 3 Jul 2026 08:31:26 UTC (132 KB) Full-text links: Access Paper: View a PDF of the paper titled Computational Complexity of the Recoverable Robust Shortest Path Problem with Discrete Recourse, by Marcel Jackiewicz and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CC < prev | next > new | recent | 2024-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from