Generic CDCL -– A Formalization of Modern Propositional Satisfiability Solvers

Abstract

Modern propositional satisfiability (or SAT) solvers are very powerful due to recent developments on the underlying data structures, the used heuristics to guide the search, the deduction techniques to in- fer knowledge, and the formula simplification techniques that are used during pre- and inprocessing. However, when all these techniques are put together, the soundness of the combined algorithm is not guaranteed any more. In this paper we present a small set of rules that allows to model modern SAT solvers in terms of a state transition system. With these rules all techniques which are applied in modern SAT solvers can be adequately modeled. Finally, we compare Generic CDCL with related systems.

Full text

Generic CDCL – A Formalization of Modern
Propositional Satisfiability Solvers
Steffen H¨
olldobler, Norbert Manthey, Tobias Philipp and Peter Steinke
International Center for Computational Logic
Technische Universit¨
at Dresden
Abstract. Modern propositional satisfiability (or SAT) solvers are very
powerful due to recent developments on the underlying data structures,
the used heuristics to guide the search, the deduction techniques to in-
fer knowledge, and the formula simplification techniques that are used
during pre- and inprocessing. However, when all these techniques are
put together, the soundness of the combined algorithm is not guaranteed
any more. In this paper we present a small set of rules that allows to
model modern SAT solvers in terms of a state transition system. With
these rules all techniques which are applied in modern SAT solvers can
be adequately modeled. Finally, we compare Generic CDCL with related
systems.
1 Introduction
Many practical problems of computer science are in the complexity class NP.
There are many well studied formalisms that can handle problems of this class,
among them are constraint satisfaction [20], answer set programming [7], or
satisfiability checking [3]. Although the two former formalisms admit a richer
language, the latter approach is still very competitive even if the expressivity of
its language is comparatively low.
The propositional satisfiability problem (SAT) consists of a propositional for-
mula and asks whether there is a satisfying assignment for the Boolean variables
occurring in the formula. From a complexity theory point of view SAT is NP-
complete [4] and, thus, intractable. Still, there are many industrial and academic
applications that can be solved nicely with modern SAT solvers. For instance a
SAT-based railway scheduling software outperformed the native version [9]. Like-
wise, haplotype matching [13] can be solved nicely with modern SAT solvers.
The success of the SAT approach lies in the strength of today’s SAT solvers.
SAT solvers do not operate on testing all possible variable assignments, but
on constructing an assignment by successively interleaving two processes, viz.,
guessing and propagating the assignment of literals. The main inference rule is
unit propagation, an efficient form of resolution. Combined with a decision rule
it is the core of the basic algorithm known as the DPLL algorithm [5]. In the
case that a contradiction is found in the formula with respect to the current
variable assignment, advanced SAT solvers backtrack and learn a conflict clause

which prevents the current and similar conflicts. With the addition of so-called
learned clauses the basic algorithm is known as CDCL algorithm [16].
Modern systematic SAT solvers are highly tuned and complex proof pro-
cedures employing many advanced techniques like clause learning [16], non-
chronological backtracking,restarts [8], clause removal [2, 6], decision heuris-
tics [2,17], and formula simplification techniques [12]. Specialized, cache-conscious
data structures [10] further improve the performance. This way, today’s solvers
like Riss,1MiniSAT or Lingeling can handle formulas with millions of variables
and millions of clauses.
However, the success of modern solvers carries a price tag: increased code
complexity. Successful SAT solvers like the above mentioned ones consist of mul-
tiple thousand lines of code and are written in programming languages with
side effects like C or C++. Due to the code complexity, the behavior of SAT
solvers is hard to understand and state-of-the-art SAT solver internals are hard
to teach. Moreover, finding additional techniques and integrating them into a
SAT solver is getting more complex, as we have to consider the interplay with
all the remaining techniques. Consequently, abstracting from specific algorithms,
data structures, and heuristics is extremely important in order to discover and
prove properties of a modern SAT solver as well as to understand the principles
of SAT solving.
This problem was tackled by different formalizations, notably Linearized
DPLL [1], Rule-based SAT Solver Descriptions [15], and Abstract
DPLL [18]. However, these systems do not appropriately model modern SAT
solvers anymore. In particular, preprocessing and applying preprocessing tech-
niques interleaved with search, known as inprocessing, became a crucial part in
SAT solving. Applying formula simplification techniques also during search is an
attractive idea since it allows to use valuable formula simplifications while taking
learned clauses into account. For example, the SAT solver Lingeling benefits
considerably from this approach.
The contribution of this paper is the formalism Generic CDCL that models
the computation of modern SAT solvers. Equipped with a small set of simple
state transition rules, we can model all well-established techniques like prepro-
cessing, inprocessing, restarts, clause sharing, as well as clause learning and for-
getting. This formalism allows us to reason about the behavior of SAT solvers
independently of the specific implementation. Additionally, the framework is a
first step to explain how modern SAT solvers are working in a compact and easy
way. Besides the presentation of Generic CDCL, the main result of this paper is
the proof that Generic CDCL and, consequently, all its instances are sound.
The paper is structured as follows: In Section 2 we describe basic concepts of
satisfiability testing. We present Generic CDCL in Section 3, where we also prove
that Generic CDCL correctly solves the satisfiability problems. Afterwards, we
compare Generic CDCL with related formalism in Section 4 and we conclude
the paper in Section 5.
1The SAT solver Riss is freely available at tools.computational-logic.org.

2 Preliminaries
2.1 The Satisfiability Problem
We assume a fixed infinite set Vof Boolean variables. A literal is a variable v
(positive literal ) or a negated variable v(negative literal). The complement xof
a positive (negative, resp.) literal xis the negative (positive, resp.) literal with
the same variable as x. The complement of a set Sof literals, denoted with S,
is defined as S={x|x∈S}. Finite sets of clauses are called formulas, where
aclause is a finite set of literals. Sometimes, we write a clause {x1, . . . , xn}also
as the disjunction (x1∨. . . ∨xn) and a formula {C1, . . . , Cn}as the conjunction
(C1∧. . . ∧Cn). The empty clause is denoted by ⊥, the empty formula by >. The
formula obtained from Fby replacing all occurrences of the variable vby the
variable wis denoted by F[v7→ w]. The set of all variables occurring in a formula
F(in positive or negative literals) is denoted by vars(F); the set of all literals
occurring in Fby lits(F). For instance, if x, y ∈ V , then F={{x, y},{y}} is
a formula, its alternative representation using logical connectives is (x∨y)∧y,
vars(F) = {x, y}, and lits(F) = {x, y}.
The semantics of formulas is based on the notion of an interpretation. An
interpretation Iis a set of literals which does not contain a complementary pair
x,xof literals. An interpretation Iis total iff for each v∈ V either v∈Ior v∈I.
The satisfaction relation |= is defined as follows: Let Ibe an interpretation, then
I|=>,I6|=⊥,I|= (x1∨. . . ∨xn) iff I|=xifor some i∈ {1, . . . , n}, and
I|= (C1∧. . . ∧Cn) iff I|=Cifor all i∈ {1, . . . , n}. Interpretation Iis a model
for the formula Fiff I|=F. In the case that a formula Fhas a model, then F
is satisfiable, otherwise it is unsatisfiable.
We relate formulas by three relations: the entailment, the equivalence and
the equisatisfiability relation: Formula Fentails formula F0iff every total model
of Fis a model of F0. Two formulas Fand F0are equivalent, in symbols F≡F0,
iff Fentails F0and F0entails F. Two formulas Fand F0are equisatisfiable, in
symbols F≡sat F0, iff either both are satisfiable or both are unsatisfiable.
For instance, the interpretation I={x, ¬z}is a model of the formula F1=
(x∨y)∧(x∨z) and, therefore, Fis satisfiable. The formula F2=x∧xhas
no model and, therefore, is unsatisfiable. The formula F3=x∧zis satisfiable
and, therefore, the formulas F1and F3are equisatisfiable, but the formulas F1
and F2are not equisatisfiable. In fact, F3|=F1since every total model Iof the
formula F3must contain xand zand, hence, the two clauses of F1are satisfied
by I. Finally, we find for all clauses Cand formulas Fthat C∨ > ≡ > ∨ C≡ >,
C∨ ⊥ ≡ ⊥ ∨ C≡C,F∧ > ≡ > ∧ F≡F, and F∧ ⊥ ≡ ⊥ ∧ F≡ ⊥.
Let xbe a literal, C= (x∨C0) and D= (x∨D0) be two clauses. Then the
clause (C0∨D0) is the resolvent of the clauses Cand Dupon the literal x. A
linear resolution derivation from the clause Cto the clause Din the formula F
is a finite sequence of clauses (Ci|1≤i≤n) such that C1=C,Cn=Dand
Ciis a resolvent of the clause Ci−1and some clause in the formula Ffor all
i∈ {2, . . . , n −1}; one should observe that Fentails Dand that the addition of
entailed clauses to a formula preserves equivalence.

2.2 Variable Assignments and the Reduct Operator
Let Jbe a finite sequence of literals. In Jeach literal may be marked as a
decision literal by placing a dot on top like in ˙x; if a literal xis not marked, then
it is a propagation literal. Let Jbe a sequence of literals of length n. We say that
literal x∈Jiff there is a k∈ {1, . . . , n}such that x=xk. Let J1= (x1, . . . , xn)
and J2= (y1, . . . , ym) be two sequences of literals; their concatenation J1J2is
the sequence (x1, . . . , xn, y1, . . . , ym). If a finite sequence Jof literals does not
contain a complementary pair of literals, then Jrepresents an interpretation. As
this condition is always met in this paper, we identify sequences of literals with
interpretations whenever appropriate.
The reduct of a formula Fw.r.t. an interpretation J, in symbols F|J, is
defined as F|J:= {C|J|C∈Fand for every literal x∈Cwe find that x6∈ J},
where C|J=C\ {x|x∈J}. Intuitively, the reduct operator expresses the state
of a SAT solver, where the formula Fis the working formula and Jis the working
assignment. For instance, let F={{x, y},{z}}, then F|x={{y},{z}},F|z=
{{x, y},⊥} and F|y z =>, where the interpretations are written as sequences
of literals. One should observe that the reduct operator does not distinguish
between propagation and decision literals.
Lemma 1 below summarizes the properties of the reduct operator: (i) The
reduct is monotone. (ii) A formula Fis satisfiable iff there exists an interpre-
tation Jsuch that the reduct of a formula w.r.t. Jis the empty formula. (iii)
If the reduct of a formula Fw.r.t. the interpretation {x}is unsatisfiable, and
the formula Fentails the literal x, then the formula Fis unsatisfiable. (iv) The
reduct operator is a semantic operator in the sense that it cannot distinguish
equivalent formulas.
Lemma 1 (Reduct Operator). Let F, F 0be formulas and xa literal.
(i) F|J⊆(F∪F0)|Jfor every interpretation J.
(ii) Fis satisfiable iff there exists a Jsuch that F|J=>.
(iii) If F|xis unsatisfiable and F|=x, then Fis unsatisfiable.
(iv) If F≡F0, then F|J≡F0|Jfor every interpretation J.
Proof. See [19, pp.10–12]. ut
3 Generic CDCL
Modern SAT solvers are based on the linearized DPLL [5] algorithm and consists
of the following components: termination criteria, a decision component that
picks the branching literals, an inference component that adds propagation lit-
erals to the working sequence of literals, a backtracking component that rolls
back wrong decisions and a formula management component that simplifies the
working formula. We maintain two data structures during the execution of mod-
ern SAT solvers: the working formula, and the working set of literals. Together
they define the state. The components are modelled as a transition relation over
the set of states; the union of the rules in Fig. 1 is then the transition relation

SAT-rule: FJ;SAT SAT iff F|J=>.
UNSAT-rule: FJ;UNSAT UNSAT iff
⊥ ∈ F|Jand Jcontains only propagation literals.
DEC-rule: FJ;DEC FJ˙xiff
x∈vars(F)∪vars(F) and {x, x} ∩ J=∅.
INF-rule: FJ;INF FJ x iff
F|J≡sat F|J x and {x, x} ∩ J=∅.
LEARN-rule: FJ;LEARN F∪ {C}Jiff F|=C.
FORGET-rule: FJ;FORGET F\ {C}Jiff F\ {C} |=C.
BACK-rule: FJ J0
;BACK FJ.
INP-rule: Fε;INP F0εiff F≡sat F0.
Fig. 1: Transition relations of Generic CDCL. These relations apply to all for-
mulas Fand F0, clauses C, literals xand lists of literals Jand J0.εdenotes the
empty sequence of literals.
of Generic CDCL: Formally, we model the computation of modern SAT solvers
by means of state transition systems as follows:
Definition 2 (Generic CDCL). Generic CDCL is a state transition system
whose sets of states is
{FJ|Fis a formula and Jis a sequence of literals}∪{SAT,UNSAT},
whose initial state for the input formula Fis init(F) = Fε, whose set of
terminal states is {SAT,UNSAT}, and whose transition relation ;is defined as:
;:= {;SAT,;UNSAT,;DEC,;INF ,;LEARN,;FORGET,;BACK ,;INP}.
The SAT-rule terminates the computation with the output SAT, if the reduct of
the working formula w.r.t. the working set of literals is the empty formula. This
condition can be decided in linear time w.r.t. the size of the working formula F.
By Lemma 1 (ii) the working formula is then satisfiable.
The UNSAT-rule terminates the computation with the output UNSAT, if no
model of the working formula exists. This is the case when a conflict occurs in
the top level, i.e. ⊥ ∈ F|Jand the sequence Jof literals contains only propagation
literals. These conditions can be decided in polynomial time.
The DEC-rule extends the working sequence of literals by an unassigned literal
˙xas a decision literal.

The INF-rule extends the working list of literals by a propagation literal x, if
the reducts of the working formula w.r.t. the working sequence of literals and its
extension are equisatisfiable.
The BACK-rule models backtracking, as well as backjumping and restarts, by
deleting outermost right literals in the working sequence of literals.
The LEARN-rule adds a clause Cto the working formula, if it is entailed by
the working formula F. Deciding whether F|=Cholds, is coNP-complete.
Similarly to the INF-rule, SAT solvers avoid this check by using techniques for
creating the clause Cthat ensure this property, as for example resolution.
The FORGET-rule deletes a clause Cof the working formula F, if F\ {C} |=C.
The question whether F\ {C} |=Cholds is coNP-complete. Typically, we use
tractable algorithms to identify redundant clauses. For instance, clauses that
were introduced by the LEARN-rule but have turned out to be useless and did
not participate in the elimination of other clauses in the formula can be removed.
For more details on the deletion of clauses see [12].
The INP-rule models formula simplifications that are used in pre- and inpro-
cessing. It replaces the working formula with an equisatisfiable formula when
the working sequence of literals is empty.
Let ∗
;be the reflexive and transitive closure of ;. Let x0
;xfor all states x,
and xn
;zfor all natural numbers n∈Nif and only if there exists a state ysuch
that xn−1
;y;z. In the next subsection we investigate the question whether
Generic CDCL correctly solves the SAT problem. Formally, we define Generic
CDCL to be sound iff for all formulas F0we have that init(F0)∗
;SAT implies
that F0is satisfiable and init(F0)∗
;UNSAT implies that F0is unsatisfiable.
3.1 Generic CDCL is Sound
Before proceeding to the soundness proof of Generic CDCL, it will be necessary
to study two invariants of Generic CDCL that are presented in the proposition
below: (i) states that the rules of Generic CDCL do not change the satisfiability
of the working formula, and (ii) states that whenever the working sequence of
literals is of the form J1x J2, where xis a propagation literal, the reducts of the
working formula w.r.t. J1and J1xare equisatisfiable.
Proposition 3 (Invariants). Let F0, F be formulas, Jbe a sequence of literals,
and n∈N. If init(F0)n
;FJ, then
1. F0≡sat F, and
2. F|J1≡sat F|J1x, for all sequences of literals J1, J2and propagation literals x
with J=J1x J2.
Proof. The claims are proven by induction on n. For the base case n= 0, 1.
follows from F0=Fand 2. holds since the Jis empty. For the induction step,
assume that the claim holds for the state FJand suppose that FJ;RF0J0
where R∈ {DEC,INF,LEARN,FORGET,BACK,INP}:

–DEC-rule: In this case, F0=Fand J0=J˙xfor some decision literal ˙x
with {x, x} ∩ J=∅.1. follows since F0≡sat Fholds by induction. 2. holds
because the appended literal is a decision literal. Formally, let J0
1, J 0
2be literal
sequences, ybe a propagation literal such that J0=J0
1y J0
2˙x. By induction,
we conclude that F|J0
1≡sat F|J0
1y. Hence, F0|J0
1≡sat F0|J1y.
–BACK-rule: In this case, F0=Fand J=J0J00.1. follows since F0≡sat Fby
induction. 2. holds because the literal sequence J0is a prefix of J. Formally,
let J0
1, J 0
2be literal sequences and ybe a propagation literal such that J0=
J0
1y J0
2. By induction, we conclude that F|J0
1≡sat F|J0
1y, and consequently
we know that F0|J0
1≡sat F0|J0
1y.
–LEARN-rule: In this case, F0=F∪ {C}where F|=Cand J0=J.1. follows
since F≡F0and the addition of the entailed clause Cpreserves equivalence
of the working formula. 2. follows from the reduct operator being a semantic
operator by Lemma 1.iv and therefore F0|J0
1≡sat F0|J0
1yholds by induction
for every literal sequences J0
1, J 0
2and propagation literals ywith J0=J0
1y J0
2.
–FORGET-rule: This case can be treated as in the LEARN-rule.
–INF-rule: In this case, F0=Fand J0=J x for some propagation literal x
with {x, x} ∩ J=∅.1. follows since F0≡sat Fholds by induction. 2. follows
from the definition of the INF-rule: Consider the literal sequences J0
1, J 0
2and a
propagation literal ysuch that J0=J0
1y J0
2. In the case that y=x, we know
that J0
2is the empty sequence of literals and consequently F|J0
1≡sat F|J0
1y
holds by the definition of the INF-rule. In the case of y6=x, we can conclude
the claim by induction.
–INP-rule: In this case, F0≡sat Fand J0is the empty sequence. Consequently,
1. holds by the definition of the INP-rule, and 2. is satisfied as J00 =ε.ut
We can now show the main theorem in this paper.
Theorem 4 (Soundness). Generic CDCL is sound.
Proof. We divide the proof in two parts, first proving that the output SAT is cor-
rect, and then proving that the output UNSAT is correct. Let F0, F be formulas,
Jbe a sequence of literals and suppose that
init(F0)∗
;FJ;SAT(UNSAT,resp.).
SAT By the definition of the SAT-rule, we know that F|J=>. By Lemma 1.ii,
we know that the formula Fis satisfiable. From the result that the formula Fis
satisfiable together with the property that the formulas F0and Fare equisatis-
fiable given in Prop. 3(1.), we conclude that the input formula F0is satisfiable.
UNSAT By the definition of the UNSAT-rule, we know that ⊥ ∈ F|Jand the
working sequence of literals J= (x1. . . xn) contains only propagation literals.
Since a conflict occurs, F|Jis unsatisfiable. From the result that the formula F|J
is unsatisfiable and the literal sequence Jcontains only propagation literals we
can repeatably apply Prop. 3(2.) and we obtain that the formula Fis unsat-
isfiable. Since the formula Fis unsatisfiable and the formulas Fand F0are
equisatisfiable by Prop. 3(1.), we conclude that F0is unsatisfiable. ut

3.2 Generic CDCL Subsumes Important SAT Solving Techniques
We now describe some important SAT solving techniques, and demonstrate that
Generic CDCL can adequately model these techniques.
Subsumption For a formula F, the clause C∈Fsubsumes the clause D∈F
iff C⊂D. In this case, Dcan be deleted because F\ {D} |=D. Consequently,
FJ;FORGET F\{D}Jholds for every literal sequence J. Removing subsumed
clauses is done as a preprocessing step in SAT solvers and during clause learning.
Tautologies A clause Cis a tautology if it contains a complementary pair of lit-
erals. Every formula Fentails a tautology and the LEARN-rule in Generic CDCL
subsumes this techniques. Tautologies are eliminated during preprocessing.
Conflict-Directed Backtracking and Learning [21] is an improvement of naive
backtracking that takes the reason of the conflict into account. Consider the
state FJ˙x J0and a clause C∈Fwhere C|J˙x J 0=⊥. The clause Cis the
conflict clause. If there is a linear resolution derivation from the conflict clause C
to a clause Din the formula Fsuch that D|Jis the unit clause y, the technique
rewrites the state FJ˙x J0into the state F∪ {C}J y. Conflict-directed
backtracking and learning can be simulated by the following transition steps:
FJ˙x J0;BACK FJ;LEARN F∪ {D}J;INF F∪ {D}J y.
Blocked Clause Elimination [11] A clause Cis blocked in the formula Fif it
contains a literal xsuch that all resolvents of the clause Cand clauses D∈F
upon xare tautological. Blocked clauses are removed from a formula during pre-
and inprocessing. If Cis blocked in F, then F≡sat F\ {C}and, therefore, the
INP-rule subsumes the blocked clause elimination technique.
Unit Propagation A clause that contains a single literal is a unit clause. Unit
propagation adds the propagation literal xto the literal sequence J, whenever
the reduct of the working formula w.r.t. Jcontains the unit clause (x). Since
F|J|=x, we know that F|J≡sat F|J x and consequently the INF-rule subsumes
unit resolution.
Pure Literal A literal xis pure in the formula F, if x6∈ lits(F). For pure
literals, it holds that F≡sat F|xand, therefore, whenever a literal xis pure in
the formula F|Jfor some literal sequence J, Generic CDCL can add the pure
literal to the working literal sequence: FJ;INF FJ x.
4 Related Work
Several attempts have been made to formalize sequential SAT solvers in terms of
transition systems or proofs calculi: Abstract DPLL [18], Linearized DPLL [1],
and Rule-based SAT solver description [15]. In general, these formalization are
based on a notion of state like Generic CDCL.
However, these formalizations cannot adequately model recent SAT solvers:
For instance, Linearized DPLL does not model the SAT solver MiniSAT, because

Linearized DPLL restricts decision literals to occur in the working formula, but
the solver MiniSAT can also select the complements of such literals. Addition-
ally, Linearized DPLL does not model formula simplification techniques such
as blocked clause elimination, or probing-based inference techniques. Similarly,
Abstract DPLL and the Rule-based SAT solver description [15] do not model
formula simplifications that changes the semantics of formulas like blocked clause
elimination. Maric highlighted the implementation of clause learning techniques
in his Rule-based SAT solver description [15], but it does not include recent devel-
opments such as clause strengthening. All these formalizations consider DPLL-
based SAT solvers, but the ancient pure literal rule is not subsumed in these
systems. In contrast, Generic CDCL subsumes all recent SAT techniques to the
best of our knowledge.
In [12] Jarvisalo et. al. developed a formal system to model clause learning,
forgetting and formula simplification techniques to understand the side-effects
of the combination of different rules. They drew our attention to the interplay
of the learned clause database with inprocessing techniques. The interplay of
clause sharing and formula simplification techniques in parallel SAT solvers was
analyzed in [14], where the state of a sequential SAT solver was modelled just as
the working formula. We believe that Generic CDCL is an important fragment
to understand sequential SAT solvers with inprocessing and their cooperation in
the parallel-portfolio setting with clause sharing.
5 Conclusion
The propositional satisfiability problem is of great practical interest and can be
efficiently answered by modern SAT solvers like Riss,Lingeling or MiniSAT. To-
day, modern SAT solvers are highly tuned proof procedures with many advaned
techniques. Therefore, it is desireable to investigate SAT solving techniques in
combination with each other and to abstract from implementations.
In this paper, we developed Generic CDCL, a formalism that models the
computation of modern SAT solvers in terms of a state transition system, where
each transition rule abstracts a component in a SAT solver. In particular, the
transition rules INF and INP model formula simplification techniques like blocked
clause elimination and inference techniques such as the pure literal rule. We
have examined invariants in Generic CDCL and have shown that Generic CDCL
is sound. In contrast to previous work on formalizations of SAT solvers, we
can model all recent techniques. The findings add to our understanding of the
interplay of inprocessing techniques with the other components of SAT solvers.
A limitation of Generic CDCL is its lack of details in the learning and in-
ference component. As future work, we plan to investigate properties such as
completeness,confluence and termination.
References
1. Arnold, H.: A linearized DPLL calculus with clause learning. Tech. rep., Universit¨
at
Potsdam. Institut f¨
ur Informatik (2009)

2. Audemard, G., Simon, L.: Predicting learnt clauses quality in modern SAT solvers.
In: Boutilier, C. (ed.) IJCAI 2009. pp. 399–404. Morgan Kaufmann Publishers Inc.,
Pasadena (2009)
3. Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability.
IOS Press, Amsterdam (2009)
4. Cook, S.A.: The complexity of theorem-proving procedures. In: Harrison, M.A.,
Banerji, R.B., Ullman, J.D. (eds.) STOC 1991. pp. 151–158. ACM (1971)
5. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving.
Commun. ACM 5(7), 394–397 (1962)
6. E´en, N., S¨
orensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella,
A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)
7. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In:
Kowalski, R.A., Bowen, K.A. (eds.) ICLP 1988. pp. 1070–1080. MIT Press (1988)
8. Gomes, C.P., Selman, B., Crato, N., Kautz, H.: Heavy-tailed phenomena in satis-
fiability and constraint satisfaction problems. J. Autom. Reason. 24(1–2), 67–100
(2000)
9. Großmann, P., H¨
olldobler, S., Manthey, N., Nachtigall, K., Opitz, J., Steinke, P.:
Solving periodic event scheduling problems with SAT. In: Jiang, H., Ding, W.,
Ali, M., Wu, X. (eds.) IEA / AIE 2012. LNCS, vol. 7345, pp. 166–175. Springer,
Heidelberg (2012)
10. H¨
olldobler, S., Manthey, N., Saptawijaya, A.: Improving resource-unaware SAT
solvers. In: Ferm¨
uller, C.G., Voronkov, A. (eds.) LPAR 2010. LNCS, vol. 6397, pp.
519–534. Springer, Heidelberg (2010)
11. J¨
arvisalo, M., Biere, A., Heule, M.: Blocked clause elimination. In: Esparza, J., Ma-
jumdar, R. (eds.) TACAS 2010. LNCS, vol. 6015, pp. 129–144. Springer, Heidelberg
(2010)
12. J¨
arvisalo, M., Heule, M.J.H., Biere, A.: Inprocessing rules. In: Gramlich, B., Miller,
D., Sattler, U. (eds.) IJCAR 2012. LNCS, vol. 7364, pp. 355–370. Springer, Hei-
delberg (2012)
13. Lynce, I., Marques-Silva, J.: Efficient haplotype inference with Boolean satisfiabil-
ity. In: AAAI 2006. pp. 104–109. AAAI Press (2006)
14. Manthey, N., Philipp, T., Wernhard, C.: Soundness of inprocessing in clause sharing
SAT solvers. In: J¨
arvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962,
pp. 22–39. Springer, Heidelberg (2013)
15. Mari´c, F.: Formalization and implementation of modern SAT solvers. J. Autom.
Reason. 43(1), 81–119 (2009)
16. Marques Silva, J.P., Sakallah, K.A.: GRASP: A search algorithm for propositional
satisfiability. IEEE Transactions on Computers 48(5), 506–521 (1999)
17. Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineer-
ing an efficient SAT solver. In: DAC 2001. pp. 530–535. ACM, New York (2001)
18. Nieuwenhuis, R., Oliveras, A., Tinelli, C.: Abstract DPLL and abstract DPLL
modulo theories. In: Baader, F., Voronkov, A. (eds.) LPAR 2004. LNCS, vol. 3452,
pp. 36–50. Springer, Heidelberg (2005)
19. Philipp, T.: Expressive Models for Parallel Satisfiability Solvers. Master thesis,
Technische Universit¨
at Dresden (2013)
20. Rossi, F., Beek, P.v., Walsh, T.: Handbook of Constraint Programming. Elsevier
Science Inc., New York (2006)
21. Silva, J.P.M., Sakallah, K.A.: GRASP - a new search algorithm for satisfiability.
In: ICCAD 1996. pp. 220–227. IEEE Computer Society, Washington (1996)