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Algorithmic Discrepancy Minimization

11/18/2019

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by Michael Whitmeyer, et al.

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This report will be a literature review on a result in algorithmic discrepancy theory. We will begin by providing a quick overview on discrepancy theory and some major results in the field, and then focus on an important result by Shachar Lovett and Raghu Meka. We restate the main algorithm and ideas of the paper, and rewrite proofs for some of the major results in the paper.

Michael Whitmeyer
4 publications
Jonathan Liu
1 publication

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research

∙ 11/10/2022

Discrepancy Minimization via Regularization

We introduce a new algorithmic framework for discrepancy minimization ba...

0 Lucas Pesenti, et al. ∙

research

∙ 09/15/2023

The discrepancy of greater-than

The discrepancy of the n × n greater-than matrix is shown to be π/2 ln n...

0 Srikanth Srinivasan, et al. ∙

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A note on the periodic L_2-discrepancy of Korobov's p-sets

We study the periodic L_2-discrepancy of point sets in the d-dimensional...

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research

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A Unified Approach to Discrepancy Minimization

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0 Nikhil Bansal, et al. ∙

research

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Balancing Gaussian vectors in high dimension

Motivated by problems in controlled experiments, we study the discrepanc...

0 Raghu Meka, et al. ∙

research

∙ 05/10/2020

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1 Ben Goertzel, et al. ∙

research

∙ 07/19/2019

Algorithmic Distortion of Informational Landscapes

The possible impact of algorithmic recommendation on the autonomy and fr...

0 Camille Roth, et al. ∙