Optimal sparsification for some binary CSPs using low-degree polynomials

Bart M.P. Jansen (Corresponding author), Astrid Pieterse (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)


This article analyzes to what extent it is possible to efficiently reduce the number of clauses in NP-hard satisfiability problems without changing the answer. Upper and lower bounds are established using the concept of kernelization. Existing results show that if NP coNP/poly, no efficient preprocessing algorithm can reduce n-variable instances of cnf-sat with d literals per clause to equivalent instances with O(nd-ϵ ) bits for any ϵ > 0. For the Not-All-Eqal sat problem, a compression to size O(nd-1) exists. We put these results in a common framework by analyzing the compressibility of CSPs with a binary domain. 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 polynomial-Time algorithm can reduce to an equivalent instance with O(n2-ϵ ) bits for any ϵ > 0, unless NP coNP/poly.

Original languageEnglish
Article number28
Number of pages26
JournalACM Transactions on Computation Theory
Issue number4
Publication statusPublished - Sep 2019


  • Constraint satisfaction problem
  • kernelization
  • satisfiability
  • sparsification


Dive into the research topics of 'Optimal sparsification for some binary CSPs using low-degree polynomials'. Together they form a unique fingerprint.

Cite this