@phdthesis{Ghasemzadeh2005,
  author    = {Ghasemzadeh, Mohammad},
  title     = {A new algorithm for the quantified satisfiability problem, based on zero-suppressed binary decision diagrams and memoization},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-6378},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {Quantified Boolean formulas (QBFs) play an important role in theoretical computer science. QBF extends propositional logic in such a way that many advanced forms of reasoning can be easily formulated and evaluated. In this dissertation we present our ZQSAT, which is an algorithm for evaluating quantified Boolean formulas. ZQSAT is based on ZBDD: Zero-Suppressed Binary Decision Diagram , which is a variant of BDD, and an adopted version of the DPLL algorithm. It has been implemented in C using the CUDD: Colorado University Decision Diagram package. The capability of ZBDDs in storing sets of subsets efficiently enabled us to store the clauses of a QBF very compactly and let us to embed the notion of memoization to the DPLL algorithm. These points led us to implement the search algorithm in such a way that we could store and reuse the results of all previously solved subformulas with a little overheads. ZQSAT can solve some sets of standard QBF benchmark problems (known to be hard for DPLL based algorithms) faster than the best existing solvers. In addition to prenex-CNF, ZQSAT accepts prenex-NNF formulas. We show and prove how this capability can be exponentially beneficial.},
  subject      = {Bin{\"a}res Entscheidungsdiagramm},
  language  = {en}
}
@article{Arnold2007,
  author    = {Arnold, Holger},
  title     = {A linearized DPLL calculus with learning},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-15421},
  year      = {2007},
  abstract  = {This paper describes the proof calculus LD for clausal propositional logic, which is a linearized form of the well-known DPLL calculus extended by clause learning. It is motivated by the demand to model how current SAT solvers built on clause learning are working, while abstracting from decision heuristics and implementation details. The calculus is proved sound and terminating. Further, it is shown that both the original DPLL calculus and the conflict-directed backtracking calculus with clause learning, as it is implemented in many current SAT solvers, are complete and proof-confluent instances of the LD calculus.},
  language  = {en}
}
@unpublished{Arnold2009,
  author    = {Arnold, Holger},
  title     = {A linearized DPLL calculus with clause learning (2nd, revised version)},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-29080},
  year      = {2009},
  abstract  = {Many formal descriptions of DPLL-based SAT algorithms either do not include all essential proof techniques applied by modern SAT solvers or are bound to particular heuristics or data structures. This makes it difficult to analyze proof-theoretic properties or the search complexity of these algorithms. In this paper we try to improve this situation by developing a nondeterministic proof calculus that models the functioning of SAT algorithms based on the DPLL calculus with clause learning. This calculus is independent of implementation details yet precise enough to enable a formal analysis of realistic DPLL-based SAT algorithms.},
  language  = {en}
}