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This is a Quantitative Study study.

October 3, 2025Open Access

Multipass Linear Sketches for Geometric LP-Type Problems

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This analysis reveals improvements in approximation for MEB and SVM in big data contexts, suggesting efficient streaming solutions.

The multipass algorithm achieves exponential improvements in approximation for LP-type problems, enabling high accuracy.
Space complexity is significantly reduced to polynomial in dimensionality and polylogarithmic in accuracy parameter.
Lower bounds on the limitations of single-pass algorithms emphasize the advantages of a multipass approach.
The study primarily addresses problems with combinatorial and VC dimensions, enhancing their applicability in various fields.

Çekirge et al. (2025) studied this question.

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