In this paper, we show strong hardness results that rule out any better discrepancy guarantees for efficient algorithms. We show that from the perspective of ...

Hardness for Systems of Multisets. Hardness for Systems of Sets. Extensions. Tight Hardness Results for Minimizing. Discrepancy. Moses Charikar Alantha Newman.

We show that from the perspective of computational efficiency, these results are tight for general set systems where M = O(N). Specifically, we show that it is ...

Dec 18, 2013 · We show that from the perspective of computational efficiency, these results are tight for general set systems where M = O(N). Specifically, we ...

In the Discrepancy problem, we are given M sets {S1,..., SM} on N elements. Our goal is to find an assignment χ of {−1, + 1} values to elements, ...

Feb 2, 2021 · Moses Charikar, Alantha Newman, Aleksandar Nikolov: Tight Hardness Results for Minimizing Discrepancy. SODA 2011: 1607-1614.

Nov 26, 2022 · Tight hardness results for minimizing discrepancy. In Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms ...

Tight hardness results for minimizing discrepancy. In Proceedings of the 22nd Annual ACM-SIAM Symposium on. Discrete Algorithms (SODA), 2011. [Mat10] J ...

In this note, we prove that the problem of computing the linear discrepancy of a given matrix is -hard, even to approximate within factor for any.

Our results are inspired by and analogous to the hardness results for Spencer's Discrepancy ... Tight hardness results for minimizing discrepancy. In Proceedings ...