Algorithmic Discrepancy Minimization

TL;DR

This paper reviews key results in algorithmic discrepancy theory, focusing on Lovett and Meka's algorithm, explaining its main ideas and proofs, and highlighting its significance in the field.

Contribution

It provides a comprehensive overview and detailed explanation of Lovett and Meka's discrepancy minimization algorithm, including proof rewrites and theoretical insights.

Findings

Summarizes major discrepancy theory results

Restates and explains Lovett and Meka's algorithm

Provides proof rewrites for key results

Abstract

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.