The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues
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Abstract (arXiv)
An important result in discrepancy due to Banaszczyk states that for any set of n vectors in ℝm of ℓ2 norm at most 1 and any convex body K in ℝm of Gaussian measure at least half, there exists a ± 1 combination of these vectors which lies in 5K. This result implies the best known bounds for several problems in discrepancy. Banaszczyk's proof of this result is non-constructive and a major open problem has been to give an efficient algorithm to find such a ± 1 combination of the vectors. In this paper, we resolve this question and give an efficient randomized algorithm to find a ± 1 combination of the vectors which lies in cK for c>0 an absolute constant. This leads to new efficient algorithms for several problems in discrepancy theory.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2017.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 2.
9 papers.
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- 2022Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controlscited by 1
- 2023Stronger Coreset Bounds for Kernel Density Estimators via Chainingcited by 1
- 2022Algorithms for Discrepancy, Matchings, and Approximations: Fast, Simple, and Practicalcited by 0
- 2026Self-Balancing Sequential Sampling: Fast Convergence with Controlled Predictabilitycited by 0
- 2026Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructionscited by 0
- 2026Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions6 Sep 2026
- 2026Self-Balancing Sequential Sampling: Fast Convergence with Controlled Predictability23 Jul 2026
- 2025Low-Rank Thinning17 Feb 2025
- 2023Stronger Coreset Bounds for Kernel Density Estimators via Chaining12 Oct 2023
- 2022Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls28 Nov 2022
- 2022Sample Constrained Treatment Effect Estimation12 Oct 2022
- 2022Algorithms for Discrepancy, Matchings, and Approximations: Fast, Simple, and Practical2 Sep 2022
- 2021Kernel Thinning12 May 2021
- 2021Smoothed Analysis with Adaptive Adversaries16 Feb 2021