Spanoids - An Abstraction of Spanning Structures, and a Barrier for LCCs

Authors Zeev Dvir, Sivakanth Gopi, Yuzhou Gu, Avi Wigderson

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Author Details

Zeev Dvir
  • Dept of Computer Science and Dept of Mathematics, Princeton University, Princeton, NJ, USA
Sivakanth Gopi
  • Microsoft Research, Redmond, WA, USA
Yuzhou Gu
  • MIT, Cambridge, MA, USA
Avi Wigderson
  • Institute of Advanced Study, Princeton, NJ, USA

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Zeev Dvir, Sivakanth Gopi, Yuzhou Gu, and Avi Wigderson. Spanoids - An Abstraction of Spanning Structures, and a Barrier for LCCs. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 32:1-32:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


We introduce a simple logical inference structure we call a spanoid (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry (point-line incidences), algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation), network theory (gossip / infection processes) and coding theory. We initiate a thorough investigation of spanoids, from computational and structural viewpoints, focusing on parameters relevant to the applications areas above and, in particular, to questions regarding Locally Correctable Codes (LCCs). One central parameter we study is the rank of a spanoid, extending the rank of a matroid and related to the dimension of codes. This leads to one main application of our work, establishing the first known barrier to improving the nearly 20-year old bound of Katz-Trevisan (KT) on the dimension of LCCs. On the one hand, we prove that the KT bound (and its more recent refinements) holds for the much more general setting of spanoid rank. On the other hand we show that there exist (random) spanoids whose rank matches these bounds. Thus, to significantly improve the known bounds one must step out of the spanoid framework. Another parameter we explore is the functional rank of a spanoid, which captures the possibility of turning a given spanoid into an actual code. The question of the relationship between rank and functional rank is one of the main questions we raise as it may reveal new avenues for constructing new LCCs (perhaps even matching the KT bound). As a first step, we develop an entropy relaxation of functional rank to create a small constant gap and amplify it by tensoring to construct a spanoid whose functional rank is smaller than rank by a polynomial factor. This is evidence that the entropy method we develop can prove polynomially better bounds than KT-type methods on the dimension of LCCs. To facilitate the above results we also develop some basic structural results on spanoids including an equivalent formulation of spanoids as set systems and properties of spanoid products. We feel that given these initial findings and their motivations, the abstract study of spanoids merits further investigation. We leave plenty of concrete open problems and directions.

Subject Classification

ACM Subject Classification
  • Theory of computation → Error-correcting codes
  • Locally correctable codes
  • spanoids
  • entropy
  • bootstrap percolation
  • gossip spreading
  • matroid
  • union-closed family


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