4 research outputs found
The 2CNF Boolean Formula Satisfiability Problem and the Linear Space Hypothesis
We aim at investigating the solvability/insolvability of nondeterministic
logarithmic-space (NL) decision, search, and optimization problems
parameterized by size parameters using simultaneously polynomial time and
sub-linear space on multi-tape deterministic Turing machines. We are
particularly focused on a special NL-complete problem, 2SAT---the 2CNF Boolean
formula satisfiability problem---parameterized by the number of Boolean
variables. It is shown that 2SAT with variables and clauses can be
solved simultaneously polynomial time and space for an absolute constant . This fact inspires us to
propose a new, practical working hypothesis, called the linear space hypothesis
(LSH), which states that 2SAT---a restricted variant of 2SAT in which each
variable of a given 2CNF formula appears at most 3 times in the form of
literals---cannot be solved simultaneously in polynomial time using strictly
"sub-linear" (i.e., for a certain constant
) space on all instances . An immediate consequence of
this working hypothesis is . Moreover, we use our
hypothesis as a plausible basis to lead to the insolvability of various NL
search problems as well as the nonapproximability of NL optimization problems.
For our investigation, since standard logarithmic-space reductions may no
longer preserve polynomial-time sub-linear-space complexity, we need to
introduce a new, practical notion of "short reduction." It turns out that,
parameterized with the number of variables, is
complete for a syntactically restricted version of NL, called Syntactic
NL, under such short reductions. This fact supports the legitimacy
of our working hypothesis.Comment: (A4, 10pt, 25 pages) This current article extends and corrects its
preliminary report in the Proc. of the 42nd International Symposium on
Mathematical Foundations of Computer Science (MFCS 2017), August 21-25, 2017,
Aalborg, Denmark, Leibniz International Proceedings in Informatics (LIPIcs),
Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik 2017, vol. 83, pp.
62:1-62:14, 201
Power of Counting by Nonuniform Families of Polynomial-Size Finite Automata
Lately, there have been intensive studies on strengths and limitations of
nonuniform families of promise decision problems solvable by various types of
polynomial-size finite automata families, where "polynomial-size" refers to the
polynomially-bounded state complexity of a finite automata family. In this line
of study, we further expand the scope of these studies to families of partial
counting and gap functions, defined in terms of nonuniform families of
polynomial-size nondeterministic finite automata, and their relevant families
of promise decision problems. Counting functions have an ability of counting
the number of accepting computation paths produced by nondeterministic finite
automata. With no unproven hardness assumption, we show numerous separations
and collapses of complexity classes of those partial counting and gap function
families and their induced promise decision problem families. We also
investigate their relationships to pushdown automata families of polynomial
stack-state complexity.Comment: (A4, 10pt, 21 pages) This paper corrects and extends a preliminary
report published in the Proceedings of the 24th International Symposium on
Fundamentals of Computation Theory (FCT 2023), Trier, Germany, September
18-24, 2023, Lecture Notes in Computer Science, vol. 14292, pp. 421-435,
Springer Cham, 202