Methodologies of Symbolic Computation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Pages19-33
Number of pages15
ISBN (Print)9783319999562
DOIs
Publication statusPublished - Sep 2018
Event13th International Conference on Artificial Intelligence and Symbolic Computation - Quishan Hotel, Souzhou, China
Duration: 16 Sep 201819 Sep 2018
http://aisc2018.cc4cm.org/index.html

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

Conference

Conference13th International Conference on Artificial Intelligence and Symbolic Computation
Abbreviated titleAISC 2018
CountryChina
CitySouzhou
Period16/09/1819/09/18
Internet address

Fingerprint

Algebra
Computational complexity

Keywords

  • Benchmarking
  • Computer algebra

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Davenport, J. (2018). Methodologies of Symbolic Computation. In D. Wang, J. Fleuriot, & J. Calmet (Eds.), Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings (pp. 19-33). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11110 LNAI). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-99957-9_2

Methodologies of Symbolic Computation. / Davenport, James.

Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings. ed. / Dongming Wang; Jacques Fleuriot; Jacques Calmet. Cham : Springer International Publishing, 2018. p. 19-33 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11110 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Davenport, J 2018, Methodologies of Symbolic Computation. in D Wang, J Fleuriot & J Calmet (eds), Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11110 LNAI, Springer International Publishing, Cham, pp. 19-33, 13th International Conference on Artificial Intelligence and Symbolic Computation, Souzhou, China, 16/09/18. https://doi.org/10.1007/978-3-319-99957-9_2
Davenport J. Methodologies of Symbolic Computation. In Wang D, Fleuriot J, Calmet J, editors, Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings. Cham: Springer International Publishing. 2018. p. 19-33. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-99957-9_2
Davenport, James. / Methodologies of Symbolic Computation. Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings. editor / Dongming Wang ; Jacques Fleuriot ; Jacques Calmet. Cham : Springer International Publishing, 2018. pp. 19-33 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{789c0e02b1dd48d5b9581feb57f21353,
title = "Methodologies of Symbolic Computation",
abstract = "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.",
keywords = "Benchmarking, Computer algebra",
author = "James Davenport",
year = "2018",
month = "9",
doi = "10.1007/978-3-319-99957-9_2",
language = "English",
isbn = "9783319999562",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer International Publishing",
pages = "19--33",
editor = "Dongming Wang and Jacques Fleuriot and Jacques Calmet",
booktitle = "Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings",
address = "Switzerland",

}

TY - GEN

T1 - Methodologies of Symbolic Computation

AU - Davenport, James

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

KW - Benchmarking

KW - Computer algebra

UR - http://www.scopus.com/inward/record.url?scp=85053257528&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-99957-9_2

DO - 10.1007/978-3-319-99957-9_2

M3 - Conference contribution

SN - 9783319999562

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 19

EP - 33

BT - Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Proceedings

A2 - Wang, Dongming

A2 - Fleuriot, Jacques

A2 - Calmet, Jacques

PB - Springer International Publishing

CY - Cham

ER -