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Discrepancy Minimization by Walking on the Edges Raghu Meka (IAS/DIMACS) Shachar Lovett (IAS)

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Presentation on theme: "Discrepancy Minimization by Walking on the Edges Raghu Meka (IAS/DIMACS) Shachar Lovett (IAS)"— Presentation transcript:

1 Discrepancy Minimization by Walking on the Edges Raghu Meka (IAS/DIMACS) Shachar Lovett (IAS)

2 Discrepancy 1*11* *11* ***11 1*1* *11* *11* ***11 1*1*

3 Discrepancy Examples Fundamental combinatorial concept Arithmetic Progressions

4 Discrepancy Examples Fundamental combinatorial concept Halfspaces Alexander 90: Matousek 95:

5 Discrepancy Examples Fundamental combinatorial concept Axis-aligned boxes Beck 81: Srinivasan 97:

6 Why Discrepancy? Complexity theory Communication Complexity Computational Geometry Pseudorandomness Many more!

7 Spencer’s Six Sigma Theorem Central result in discrepancy theory. Beats random: Tight: Hadamard. Spencer 85: System with n sets has discrepancy at most. “Six standard deviations suffice”

8 Conjecture (Alon, Spencer): No efficient algorithm can find one. Bansal 10: Can efficiently get discrepancy. A Conjecture and a Disproof Non-constructive pigeon-hole proof Spencer 85: System with n sets has discrepancy at most.

9 This Work Truly constructive Algorithmic partial coloring lemma Extends to other settings New elemantary constructive proof of Spencer’s result EDGE-WALK: New algorithmic tool

10 Outline 1.Partial coloring Method 2.EDGE-WALK: Geometric picture

11 Focus on m = n case. Lemma: Can do this in randomized time. Partial Coloring Method Input: Output:

12 Outline 1.Partial coloring Method 2.EDGE-WALK: Geometric picture

13 1*11* *11* ***11 1*1*1 Discrepancy: Geometric View

14 1*11* *11* ***11 1*1*1 Discrepancy: Geometric View

15 Discrepancy: Geometric View Goal: Find non-zero lattice points in Polytope view used earlier by Gluskin’ 88.

16 Claim: Will find good partial coloring. Edge-Walk Start at origin Gaussian walk until you hit a face Gaussian walk within the face Goal: Find non-zero lattice point in

17 Edge-Walk: Algorithm Gaussian random walk in subspaces Standard normal in V: Orthonormal basis change

18 Edge-Walk Algorithm Discretization issues: hitting faces Might not hit face Slack: face hit if close to it.

19 Edge-Walk: Algorithm

20 Edge-Walk: Intuition Hit cube more often! Discrepancy faces much farther than cube’s

21 Summary 1.Edge-Walk: Algorithmic partial coloring lemma 2.Recurse on unfixed variables Spencer’s Theorem

22 Open Problems Q: Other applications? General IP’s, Minkowski’s theorem? Some promise: our PCL “stronger” than Beck’s Q:

23 Thank you

24 Main Partial Coloring Lemma Algorithmic partial coloring lemma


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