Methodologies of Symbolic Computation

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding


The methodologies of computer algebra are about making algebra (in the broad sense) algorithmic, and efficient as well. There are ingenious algorithms, even in the obvious settings, and also mechanisms where problems are translated into other (generally smaller) settings, solved there, and translated back. Much of the efficiency of modern systems comes from these translations. One of the major challenges is sparsity, and the complexity of algorithms in the sparse setting is often unknown, as many problems are NP-hard, or much worse. In view of this, it is argued that the traditional complexity-theoretic method of measuring progress has its limits, and computer algebra should look to the work of the SAT community, with its large families of benchmarks and serious contests, for lessons.

Original languageEnglish
Title of host publicationArtificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings
EditorsDongming Wang, Jacques Fleuriot, Jacques Calmet
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages15
ISBN (Print)9783319999562
Publication statusPublished - Sept 2018
Event13th International Conference on Artificial Intelligence and Symbolic Computation - Quishan Hotel, Souzhou, China
Duration: 16 Sept 201819 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11110 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Artificial Intelligence and Symbolic Computation
Abbreviated titleAISC 2018
Internet address


  • Benchmarking
  • Computer algebra

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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