Towards a constructive version of Banaszczyk's vector balancing theorem

Daniel Dadush, Shashwat Garg, Shachar Lovett, Aleksandar Nikolov

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4 Citations (Scopus)
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Abstract

An important theorem of Banaszczyk (Random Structures & Algorithms-98) states that for any sequence of vectors of ℓ2 norm at most 1/5 and any convex body K of Gaussian measure 1/2 in ℝn, there exists a signed combination of these vectors which lands inside K. A major open problem is to devise a constructive version of Banaszczyk's vector balancing theorem, i.e.To find an efficient algorithm which constructs the signed combination. We make progress towards this goal along several fronts. As our first contribution, we show an equivalence between Banaszczyk's theorem and the existence of O(1)-subgaussian distributions over signed combinations. For the case of symmetric convex bodies, our equivalence implies the existence of a universal signing algorithm (i.e. independent of the body), which simply samples from the subgaussian sign distribution and checks to see if the associated combination lands inside the body. For asymmetric convex bodies, we provide a novel recentering procedure, which allows us to reduce to the case where the body is symmetric. As our second main contribution, we show that the above framework can be efficiently implemented when the vectors have length O(1/√log n), recovering Banaszczyk's results under this stronger assumption. More precisely, we use random walk techniques to produce the required O(1)-subgaussian signing distributions when the vectors have length O(1/√log n), and use a stochastic gradient ascent method to implement the recentering procedure for asymmetric bodies.

Original languageEnglish
Title of host publicationApproximation, randomization, and combinatorial optimization: algorithms and Techniques - 19th International Workshop, APPROX 2016 and 20th International Workshop, RANDOM 2016
EditorsKlaus Jansen, Claire Mathieu, José D.P. Rolim, Chris Umans
Place of PublicationDagstuhl
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Number of pages12
ISBN (Electronic)978-3-95977-018-7
DOIs
Publication statusPublished - 1 Sep 2016
Event19th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2016 and the 20th International Workshop on Randomization and Computation, RANDOM 2016 - Paris, France
Duration: 7 Sep 20169 Sep 2016

Publication series

NameLeibniz International Proceedings in Informatics (LIPIcs)
Volume60
ISSN (Print)1868-8969

Conference

Conference19th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2016 and the 20th International Workshop on Randomization and Computation, RANDOM 2016
CountryFrance
CityParis
Period7/09/169/09/16

Keywords

  • Convex Geometry
  • Discrepancy
  • Vector Balancing

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