53 research outputs found
Unsatisfiable Linear CNF Formulas Are Large and Complex
We call a CNF formula linear if any two clauses have at most one variable in
common. We show that there exist unsatisfiable linear k-CNF formulas with at
most 4k^2 4^k clauses, and on the other hand, any linear k-CNF formula with at
most 4^k/(8e^2k^2) clauses is satisfiable. The upper bound uses probabilistic
means, and we have no explicit construction coming even close to it. One reason
for this is that unsatisfiable linear formulas exhibit a more complex structure
than general (non-linear) formulas: First, any treelike resolution refutation
of any unsatisfiable linear k-CNF formula has size at least 2^(2^(k/2-1))$.
This implies that small unsatisfiable linear k-CNF formulas are hard instances
for Davis-Putnam style splitting algorithms. Second, if we require that the
formula F have a strict resolution tree, i.e. every clause of F is used only
once in the resolution tree, then we need at least a^a^...^a clauses, where a
is approximately 2 and the height of this tower is roughly k.Comment: 12 pages plus a two-page appendix; corrected an inconsistency between
title of the paper and title of the arxiv submissio
On variables with few occurrences in conjunctive normal forms
We consider the question of the existence of variables with few occurrences
in boolean conjunctive normal forms (clause-sets). Let mvd(F) for a clause-set
F denote the minimal variable-degree, the minimum of the number of occurrences
of variables. Our main result is an upper bound mvd(F) <= nM(surp(F)) <=
surp(F) + 1 + log_2(surp(F)) for lean clause-sets F in dependency on the
surplus surp(F).
- Lean clause-sets, defined as having no non-trivial autarkies, generalise
minimally unsatisfiable clause-sets.
- For the surplus we have surp(F) <= delta(F) = c(F) - n(F), using the
deficiency delta(F) of clause-sets, the difference between the number of
clauses and the number of variables.
- nM(k) is the k-th "non-Mersenne" number, skipping in the sequence of
natural numbers all numbers of the form 2^n - 1.
We conjecture that this bound is nearly precise for minimally unsatisfiable
clause-sets.
As an application of the upper bound we obtain that (arbitrary!) clause-sets
F with mvd(F) > nM(surp(F)) must have a non-trivial autarky (so clauses can be
removed satisfiability-equivalently by an assignment satisfying some clauses
and not touching the other clauses). It is open whether such an autarky can be
found in polynomial time.
As a future application we discuss the classification of minimally
unsatisfiable clause-sets depending on the deficiency.Comment: 14 pages. Revision contains more explanations, and more information
regarding the sharpness of the boun
A New Lower Bound on the Maximum Number of Satisfied Clauses in Max-SAT and its Algorithmic Applications
A pair of unit clauses is called conflicting if it is of the form ,
. A CNF formula is unit-conflict free (UCF) if it contains no pair
of conflicting unit clauses. Lieberherr and Specker (J. ACM 28, 1981) showed
that for each UCF CNF formula with clauses we can simultaneously satisfy at
least \pp m clauses, where \pp =(\sqrt{5}-1)/2. We improve the
Lieberherr-Specker bound by showing that for each UCF CNF formula with
clauses we can find, in polynomial time, a subformula with clauses
such that we can simultaneously satisfy at least \pp m+(1-\pp)m'+(2-3\pp)n"/2
clauses (in ), where is the number of variables in which are not in
.
We consider two parameterized versions of MAX-SAT, where the parameter is the
number of satisfied clauses above the bounds and . The
former bound is tight for general formulas, and the later is tight for UCF
formulas. Mahajan and Raman (J. Algorithms 31, 1999) showed that every instance
of the first parameterized problem can be transformed, in polynomial time, into
an equivalent one with at most variables and clauses. We improve
this to variables and clauses. Mahajan and Raman
conjectured that the second parameterized problem is fixed-parameter tractable
(FPT). We show that the problem is indeed FPT by describing a polynomial-time
algorithm that transforms any problem instance into an equivalent one with at
most variables. Our results are obtained using our improvement
of the Lieberherr-Specker bound above
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