eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2018-07-04
27:1
27:16
10.4230/LIPIcs.ICALP.2018.27
article
Fine-Grained Derandomization: From Problem-Centric to Resource-Centric Complexity
Carmosino, Marco L.
1
Impagliazzo, Russell
1
Sabin, Manuel
2
Department of Computer Science, University of California San Diego, La Jolla, CA, USA
Computer Science Division, University of California Berkeley, Berkeley, CA, USA
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.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol107-icalp2018/LIPIcs.ICALP.2018.27/LIPIcs.ICALP.2018.27.pdf
Derandomization
Hardness vs Randomness
Fine-Grained Complexity
Average-Case Complexity
k-Orthogonal Vectors
k-CLIQUE