You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Qute in the QBF Evaluation 2018

Issue title: SAT 2018 Competitions and Evaluations

Guest editors: Martina Seidl and Luca Pulina

Article type: Research Article

Authors: Peitl, Tomáš; * | Slivovsky, Friedrich; ** | Szeider, Stefan

Affiliations: Algorithms and Complexity Group, TU Wien, Austria. E-mails: [email protected], [email protected], [email protected]

Note: [*] Supported by the Austrian Science Fund (FWF) under grant W1255-N23.

Note: [**] Supported by the Austrian Science Fund (FWF) under grant P 27721.

Keywords: QBF

DOI: 10.3233/SAT190124

Journal: Journal on Satisfiability, Boolean Modeling and Computation, vol. 11, no. 1, pp. 261-272, 2019

Received 1 November 2018
|
Revision received 1 September 2019
|
Published: 1 September 2019
Get PDF open access

Abstract

Qute is a solver for Quantified Boolean Formulas (QBFs) based on Quantified Conflict-Driven Constraint Learning (QCDCL). Its main distinguishing feature is dependency learning, a lazy technique for relaxing restrictions on the order of variable assignments imposed by nested quantifiers. In this short note, we describe the configurations of Qute submitted to QBFEval’18, along with the parameter tuning process that went into creating them.

Show more