{"ID":2886572,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03901","arxiv_id":"2508.03901","title":"Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling","abstract":"Simulation optimization is often hindered by the high cost of running simulations. Multi-fidelity methods offer a promising solution by incorporating cheaper, lower-fidelity simulations to reduce computational time. However, the bias in low-fidelity models can mislead the search, potentially steering solutions away from the high-fidelity optimum. To overcome this, we propose ASTRO-MFDF, an adaptive sampling trust-region method for multi-fidelity simulation optimization. ASTRO-MFDF features two key strategies: (i) it adaptively determines the sample size and selects appropriate sampling strategies to reduce computational cost; and (ii) it selectively uses low-fidelity information only when a high correlation with the high-fidelity is anticipated, reducing the risk of bias. We validate the performance and computational efficiency of ASTRO-MFDF through numerical experiments using the SimOpt library.","short_abstract":"Simulation optimization is often hindered by the high cost of running simulations. Multi-fidelity methods offer a promising solution by incorporating cheaper, lower-fidelity simulations to reduce computational time. However, the bias in low-fidelity models can mislead the search, potentially steering solutions away fro...","url_abs":"https://arxiv.org/abs/2508.03901","url_pdf":"https://arxiv.org/pdf/2508.03901v1","authors":"[\"Yunsoo Ha\",\"Juliane Mueller\"]","published":"2025-08-05T20:39:25Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}