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These sample-based approximation schemes include Monte Carlo and certain randomized quasi-Monte Carlo integration methods, such as scrambled net integration. Our results can be applied to the approximation of risk-averse stochastic programs and risk-averse stochastic variational inequalities. Our numerical simulations empirically demonstrate that randomized quasi-Monte Carlo approaches based on scrambled Sobol\u2019 sequences can yield smaller bias and root mean square error than Monte Carlo methods for risk-averse optimization.<\/jats:p>","DOI":"10.1007\/s10957-025-02693-6","type":"journal-article","created":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T06:03:11Z","timestamp":1747548191000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Randomized Quasi-Monte Carlo Methods for Risk-Averse Stochastic Optimization"],"prefix":"10.1007","volume":"206","author":[{"given":"Olena","family":"Melnikov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3601-3340","authenticated-orcid":false,"given":"Johannes","family":"Milz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,18]]},"reference":[{"key":"2693_CR1","doi-asserted-by":"publisher","unstructured":"Attouch, H., Buttazzo, G., Michaille, G.: Variational Analysis in Sobolev and BV spaces, MOS-SIAM Series on Optimization, vol.\u00a017, 2nd edn. 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