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Boosting isomorphic model filtering with invariants

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364201" target="_blank" >RIV/68407700:21730/22:00364201 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/s10601-022-09336-x" target="_blank" >https://doi.org/10.1007/s10601-022-09336-x</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10601-022-09336-x" target="_blank" >10.1007/s10601-022-09336-x</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Boosting isomorphic model filtering with invariants

  • Original language description

    The enumeration of finite models is very important to the working discrete mathematician (algebra, graph theory, etc) and hence the search for effective methods to do this task is a critical goal in discrete computational mathematics. However, it is hindered by the possible existence of many isomorphic models, which usually only add noise. Typically, they are filtered out a posteriori, a step that might take a long time just to discard redundant models. This paper proposes a novel approach to split the generated models into mutually non-isomorphic blocks. To do that we use well-designed hand-crafted invariants as well as randomly generated invariants. The blocks are then tackled separately and possibly in parallel. This approach is integrated into Mace4 (the most popular tool among mathematicians) where it shows tremendous speed-ups for a large variety of algebraic structures.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LL1902" target="_blank" >LL1902: Powering SMT Solvers by Machine Learning</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    CONSTRAINTS

  • ISSN

    1383-7133

  • e-ISSN

    1572-9354

  • Volume of the periodical

    27

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    20

  • Pages from-to

    360-379

  • UT code for WoS article

    000811949800001

  • EID of the result in the Scopus database

    2-s2.0-85132391947