Indifference-Zone Relaxation Procedures for Finding Feasible Systems

[2509.04514] Indifference-Zone Relaxation Procedures for Finding Feasible Systems Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Systems and Control arXiv:2509.04514 (eess) [Submitted on 2 Sep 2025 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: Indifference-Zone Relaxation Procedures for Finding Feasible Systems Authors: Yuwei Zhou , Sigrún Andradóttir , Seong-Hee Kim , Chuljin Park View PDF HTML (experimental) Abstract: We consider the problem of finding feasible systems with respect to stochastic constraints when system performance is evaluated through simulation. Our objective is to solve this problem with high computational efficiency and statistical validity. Existing indifference-zone (IZ) procedures introduce a fixed tolerance level, which denotes how much deviation the decision-maker is willing to accept from the threshold in the constraint. These procedures are developed under the assumption that all systems' performance measures are exactly the tolerance level away from the threshold, leading to unnecessary simulations. In contrast, IZ-free procedures, which eliminate the tolerance level, perform well when systems' performance measures are far from the threshold. However, they may significantly underperform compared to IZ procedures when systems' performance measures are close to the threshold. To address these challenges, we propose the Indifference-Zone Relaxation (IZR) procedure, IZR introduces a set of relaxed tolerance levels and utilizes two subroutines for each level: one to identify systems that are clearly feasible and the other to exclude those that are clearly infeasible. We also develop the IZR procedure with estimation (IZE), which introduces two relaxed tolerance levels for each system and constraint: one matching the original tolerance level and the other based on an estimate of the system's performance measure. By employing different tolerance levels, these procedures facilitate early feasibility determination with statistical validity. We prove that IZR and IZE determine system feasibility with the desired probability and show through experiments that they significantly reduce the number of observations required compared to an existing procedure. Subjects: Systems and Control (eess.SY) ; Methodology (stat.ME) MSC classes: 62L10 ACM classes: I.6.6 Cite as: arXiv:2509.04514 [eess.SY] (or arXiv:2509.04514v2 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2509.04514 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chuljin Park [ view email ] [v1] Tue, 2 Sep 2025 22:19:26 UTC (1,241 KB) [v2] Wed, 26 Aug 2026 23:52:26 UTC (1,241 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2025-09 Change to browse by: cs cs.SY eess stat stat.ME References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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)

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