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Fine-Grained Derandomization: From Problem-Centric to Resource-Centric Complexity

Authors Marco L. Carmosino, Russell Impagliazzo, Manuel Sabin

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Author Details

Marco L. Carmosino
  • Department of Computer Science, University of California San Diego, La Jolla, CA, USA
Russell Impagliazzo
  • Department of Computer Science, University of California San Diego, La Jolla, CA, USA
Manuel Sabin
  • Computer Science Division, University of California Berkeley, Berkeley, CA, USA

Funding

  • Carmosino, Marco L.: Supported by the Simons Foundation
  • Impagliazzo, Russell: Supported by the Simons Foundation
  • Sabin, Manuel: Supported by the National Science Foundation Graduate Research Fellowship under Grant #DGE-1106400

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Marco L. Carmosino, Russell Impagliazzo, and Manuel Sabin. Fine-Grained Derandomization: From Problem-Centric to Resource-Centric Complexity. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 27:1-27:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ICALP.2018.27

Abstract

We show that popular hardness conjectures about problems from the field of fine-grained complexity theory imply structural results for resource-based complexity classes. Namely, we show that if either k-Orthogonal Vectors or k-CLIQUE requires n^{epsilon k} time, for some constant epsilon>1/2, to count (note that these conjectures are significantly weaker than the usual ones made on these problems) on randomized machines for all but finitely many input lengths, then we have the following derandomizations: - BPP can be decided in polynomial time using only n^alpha random bits on average over any efficient input distribution, for any constant alpha>0 - BPP can be decided in polynomial time with no randomness on average over the uniform distribution This answers an open question of Ball et al. (STOC '17) in the positive of whether derandomization can be achieved from conjectures from fine-grained complexity theory. More strongly, these derandomizations improve over all previous ones achieved from worst-case uniform assumptions by succeeding on all but finitely many input lengths. Previously, derandomizations from worst-case uniform assumptions were only know to succeed on infinitely many input lengths. It is specifically the structure and moderate hardness of the k-Orthogonal Vectors and k-CLIQUE problems that makes removing this restriction possible. Via this uniform derandomization, we connect the problem-centric and resource-centric views of complexity theory by showing that exact hardness assumptions about specific problems like k-CLIQUE imply quantitative and qualitative relationships between randomized and deterministic time. This can be either viewed as a barrier to proving some of the main conjectures of fine-grained complexity theory lest we achieve a major breakthrough in unconditional derandomization or, optimistically, as route to attain such derandomizations by working on very concrete and weak conjectures about specific problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Complexity classes
Keywords
  • Derandomization
  • Hardness vs Randomness
  • Fine-Grained Complexity
  • Average-Case Complexity
  • k-Orthogonal Vectors
  • k-CLIQUE

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