261 research outputs found
Sum of squares lower bounds for refuting any CSP
Let be a nontrivial -ary predicate. Consider a
random instance of the constraint satisfaction problem on
variables with constraints, each being applied to randomly
chosen literals. Provided the constraint density satisfies , such
an instance is unsatisfiable with high probability. The \emph{refutation}
problem is to efficiently find a proof of unsatisfiability.
We show that whenever the predicate supports a -\emph{wise uniform}
probability distribution on its satisfying assignments, the sum of squares
(SOS) algorithm of degree
(which runs in time ) \emph{cannot} refute a random instance of
. In particular, the polynomial-time SOS algorithm requires
constraints to refute random instances of
CSP when supports a -wise uniform distribution on its satisfying
assignments. Together with recent work of Lee et al. [LRS15], our result also
implies that \emph{any} polynomial-size semidefinite programming relaxation for
refutation requires at least constraints.
Our results (which also extend with no change to CSPs over larger alphabets)
subsume all previously known lower bounds for semialgebraic refutation of
random CSPs. For every constraint predicate~, they give a three-way hardness
tradeoff between the density of constraints, the SOS degree (hence running
time), and the strength of the refutation. By recent algorithmic results of
Allen et al. [AOW15] and Raghavendra et al. [RRS16], this full three-way
tradeoff is \emph{tight}, up to lower-order factors.Comment: 39 pages, 1 figur
Sparser Random 3SAT Refutation Algorithms and the Interpolation Problem:Extended Abstract
We formalize a combinatorial principle, called the 3XOR principle, due to Feige, Kim and Ofek [12], as a family of unsatisfiable propositional formulas for which refutations of small size in any propo-sitional proof system that possesses the feasible interpolation property imply an efficient deterministic refutation algorithm for random 3SAT with n variables and âŠ(n1.4) clauses. Such small size refutations would improve the state of the art (with respect to the clause density) efficient refutation algorithm, which works only for âŠ(n1.5) many clauses [13]. We demonstrate polynomial-size refutations of the 3XOR principle in resolution operating with disjunctions of quadratic equations with small integer coefficients, denoted R(quad); this is a weak extension of cutting planes with small coefficients. We show that R(quad) is weakly autom-atizable iff R(lin) is weakly automatizable, where R(lin) is similar to R(quad) but with linear instead of quadratic equations (introduced in [25]). This reduces the problem of refuting random 3CNF with n vari-ables and âŠ(n1.4) clauses to the interpolation problem of R(quad) and to the weak automatizability of R(lin)
Strongly Refuting Random CSPs Below the Spectral Threshold
Random constraint satisfaction problems (CSPs) are known to exhibit threshold
phenomena: given a uniformly random instance of a CSP with variables and
clauses, there is a value of beyond which the CSP will be
unsatisfiable with high probability. Strong refutation is the problem of
certifying that no variable assignment satisfies more than a constant fraction
of clauses; this is the natural algorithmic problem in the unsatisfiable regime
(when ).
Intuitively, strong refutation should become easier as the clause density
grows, because the contradictions introduced by the random clauses become
more locally apparent. For CSPs such as -SAT and -XOR, there is a
long-standing gap between the clause density at which efficient strong
refutation algorithms are known, , and the
clause density at which instances become unsatisfiable with high probability,
.
In this paper, we give spectral and sum-of-squares algorithms for strongly
refuting random -XOR instances with clause density in time or in
rounds of the sum-of-squares hierarchy, for any
and any integer . Our algorithms provide a smooth
transition between the clause density at which polynomial-time algorithms are
known at , and brute-force refutation at the satisfiability
threshold when . We also leverage our -XOR results to obtain
strong refutation algorithms for SAT (or any other Boolean CSP) at similar
clause densities. Our algorithms match the known sum-of-squares lower bounds
due to Grigoriev and Schonebeck, up to logarithmic factors.
Additionally, we extend our techniques to give new results for certifying
upper bounds on the injective tensor norm of random tensors
Recognition and Exploitation of Gate Structure in SAT Solving
In der theoretischen Informatik ist das SAT-Problem der archetypische Vertreter der Klasse der NP-vollstÀndigen Probleme, weshalb effizientes SAT-Solving im Allgemeinen als unmöglich angesehen wird.
Dennoch erzielt man in der Praxis oft erstaunliche Resultate, wo einige Anwendungen Probleme mit Millionen von Variablen erzeugen, die von neueren SAT-Solvern in angemessener Zeit gelöst werden können.
Der Erfolg von SAT-Solving in der Praxis ist auf aktuelle Implementierungen des Conflict Driven Clause-Learning (CDCL) Algorithmus zurĂŒckzufĂŒhren, dessen LeistungsfĂ€higkeit weitgehend von den verwendeten Heuristiken abhĂ€ngt, welche implizit die Struktur der in der industriellen Praxis erzeugten Instanzen ausnutzen.
In dieser Arbeit stellen wir einen neuen generischen Algorithmus zur effizienten Erkennung der Gate-Struktur in CNF-Encodings von SAT Instanzen vor, und auĂerdem drei AnsĂ€tze, in denen wir diese Struktur explizit ausnutzen.
Unsere BeitrĂ€ge umfassen auch die Implementierung dieser AnsĂ€tze in unserem SAT-Solver Candy und die Entwicklung eines Werkzeugs fĂŒr die verteilte Verwaltung von Benchmark-Instanzen und deren Attribute, der Global Benchmark Database (GBD)
Phase Transition in Matched Formulas and a Heuristic for Biclique Satisfiability
A matched formula is a CNF formula whose incidence graph admits a matching
which matches a distinct variable to every clause. We study phase transition in
a context of matched formulas and their generalization of biclique satisfiable
formulas. We have performed experiments to find a phase transition of property
"being matched" with respect to the ratio where is the number of
clauses and is the number of variables of the input formula . We
compare the results of experiments to a theoretical lower bound which was shown
by Franco and Gelder (2003). Any matched formula is satisfiable, moreover, it
remains satisfiable even if we change polarities of any literal occurrences.
Szeider (2005) generalized matched formulas into two classes having the same
property -- var-satisfiable and biclique satisfiable formulas. A formula is
biclique satisfiable if its incidence graph admits covering by pairwise
disjoint bounded bicliques. Recognizing if a formula is biclique satisfiable is
NP-complete. In this paper we describe a heuristic algorithm for recognizing
whether a formula is biclique satisfiable and we evaluate it by experiments on
random formulas. We also describe an encoding of the problem of checking
whether a formula is biclique satisfiable into SAT and we use it to evaluate
the performance of our heuristicComment: Conference version submitted to SOFSEM 2018
(https://beda.dcs.fmph.uniba.sk/sofsem2019/) 18 pages(17 without refernces),
3 figures, 8 tables, an algorithm pseudocod
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