2026-04-17T03:55:21Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/4673422025-09-15T18:25:37Zcom_2072_3622col_2072_479130

00925njm 22002777a 4500 dc Alòs Pascual, Josep author Ansótegui Gil, Carlos José author Salvia Hornos, Josep M. author Torres, Eduard author 2022 We present an extension of the OptiLog Python framework. We fully redesign the solvers module to support the dynamic loading of incremental SAT solvers with support for external libraries. We introduce new modules for modelling problems into Non-CNF format with support for Pseudo Boolean constraints, for evaluating and parsing the results of applications, and we add support for constrained execution of blackbox programs and SAT-heritage integration. All these enhancements allow OptiLog to become a swiss knife for SAT-based applications in academic and industrial environments. Supported by Grant PID2019-109137GB-C21 funded by MCIN/AEI/10.13039/501100011033, PANDEMIES 2020 by Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR), Departament d’Empresa i Coneixement de la Generalitat de Catalunya, FONDO SUPERA COVID-19 funded by Crue-CSIC-SANTANDER, and the MICNN FPU fellowship (FPU18/02929). Tool framework Satisfiability Modelling OptiLog V2: Model, Solve, Tune and Run