Tight Hardness Results for Maximum Weight Rectangles
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Abstract (arXiv)
Given n weighted points (positive or negative) in d dimensions, what is the axis-aligned box which maximizes the total weight of the points it contains? The best known algorithm for this problem is based on a reduction to a related problem, the Weighted Depth problem [T. M. Chan, FOCS'13], and runs in time O(nd). It was conjectured [Barbay et al., CCCG'13] that this runtime is tight up to subpolynomial factors. We answer this conjecture affirmatively by providing a matching conditional lower bound. We also provide conditional lower bounds for the special case when points are arranged in a grid (a well studied problem known as Maximum Subarray problem) as well as for other related problems. All our lower bounds are based on assumptions that the best known algorithms for the All-Pairs Shortest Paths problem (APSP) and for the Max-Weight k-Clique problem in edge-weighted graphs are essentially optimal.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2021The Fine-Grained Hardness of Sparse Linear Regressioncited by 12
- 2023Data-Driven Subgroup Identification for Linear Regressioncited by 6
- 2021Approximate Maximum Halfspace Discrepancycited by 3
- 2026Hardness of High-Dimensional Linear Classificationcited by 1
- 2024Enhancing binary classification: A new stacking method via leveraging computational geometrycited by 0
- 2025Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distancecited by 0
- 2026Hardness of High-Dimensional Linear Classification19 Mar 2026
- 2025Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance15 Apr 2025
- 2024Enhancing binary classification: A new stacking method via leveraging computational geometry30 Oct 2024
- 2023Data-Driven Subgroup Identification for Linear Regression29 Apr 2023
- 2021Approximate Maximum Halfspace Discrepancy25 Jun 2021
- 2021The Fine-Grained Hardness of Sparse Linear Regression6 Jun 2021