Abstract
This paper analyzes to what extent it is possible to efficiently reduce the number of clauses in NPhard satisfiability problems, without changing the answer. Upper and lower bounds are established using the concept of kernelization. Existing results show that if NP is not contained in coNP/poly, no efficient preprocessing algorithm can reduce nvariable instances of CNFSAT with d literals per clause, to equivalent instances with O(n^{depsilon}) bits for any epsilon > 0. For the NotAllEqual SAT problem, a compression to size tildeO(n^{d1}) exists. We put these results in a common framework by analyzing the compressibility of binary CSPs. We characterize constraint types based on the minimum degree of multivariate polynomials whose roots correspond to the satisfying assignments, obtaining (nearly) matching upper and lower bounds in several settings. Our lower bounds show that not just the number of constraints, but also the encoding size of individual constraints plays an important role. For example, for Exact Satisfiability with unbounded clause length it is possible to efficiently reduce the number of constraints to n+1, yet no polynomialtime algorithm can reduce to an equivalent instance with O(n^{2epsilon}) bits for any epsilon > 0, unless NP is contained in coNP/poly.
BibTeX  Entry
@InProceedings{jansen_et_al:LIPIcs:2016:6482,
author = {Bart M. P. Jansen and Astrid Pieterse},
title = {{Optimal Sparsification for Some Binary CSPs Using LowDegree Polynomials}},
booktitle = {41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016)},
pages = {71:171:14},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959770163},
ISSN = {18688969},
year = {2016},
volume = {58},
editor = {Piotr Faliszewski and Anca Muscholl and Rolf Niedermeier},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2016/6482},
URN = {urn:nbn:de:0030drops64821},
doi = {10.4230/LIPIcs.MFCS.2016.71},
annote = {Keywords: constraint satisfaction problem, sparsification, satisfiability, kernelization}
}
Keywords: 

constraint satisfaction problem, sparsification, satisfiability, kernelization 
Collection: 

41st International Symposium on Mathematical Foundations of Computer Science (MFCS 2016) 
Issue Date: 

2016 
Date of publication: 

19.08.2016 