Algorithm demonstrates optimal error rates in high-dimensional integration, indicating robustness across settings.
We present a simple universal algorithm for high-dimensional integration which has the optimal error rate (independent of the dimension) in all weighted Korobov classes both in the randomized and the deterministic setting. Our theoretical findings are complemented by numerical tests.
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Goda et al. (2025) studied this question.
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