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This sequential approach relies solely on statistical information to reduce the high-dimensional uncertainty space, which may not yield the best approximation performance. Jiang et al. (2025) introduce an optimized dimensionality reduction (ODR) approach that integrates the dimensionality reduction of random parameters with subsequent optimization problems. This integration enables two outer approximations and one inner approximation of the original problem, all represented as low-dimensional SDPs that can be solved efficiently, providing two lower bounds and one upper bound, respectively. 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