SC-Square: Future Progress with Machine Learning

Matthew England

SC-Square: Future Progress with Machine Learning

Matthew England

Research Centre for Computational Science and Mathematical Modelling

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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Abstract

The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production.
In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.

Original language	English
Title of host publication	SC-Square 2021 Proceedings
Editors	Curtis Bright, James Davenport
Publisher	CEUR Workshop Proceedings
Pages	7-16
Number of pages	10
Volume	3273
Publication status	Published - 14 Nov 2022
Event	6th International Workshop on Satisfiability Checking and Symbolic Computation: Bridging Two Communities to Solve Real Problems - College Station, United States Duration: 19 Aug 2021 → 20 Aug 2021 Conference number: 6 http://www.sc-square.org/CSA/workshop6.html

Publication series

Name	CEUR Workshop Proceedings
ISSN (Print)	1613-0073

Conference

Conference	6th International Workshop on Satisfiability Checking and Symbolic Computation
Abbreviated title	SC2 Workshop 2021
Country/Territory	United States
City	College Station
Period	19/08/21 → 20/08/21
Internet address	http://www.sc-square.org/CSA/workshop6.html

Bibliographical note

Keywords

machine learning
symbolic computation
computer algebra systems
satisfiability checking
SMT solvers

Access to Document

MEngland-MLforSMTSurveyAccepted author manuscript, 645 KBLicence: CC BY
PublishedFinal published version, 1.04 MBLicence: CC BY

https://ceur-ws.org/Vol-3273/Licence: CC BY

Cite this

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title = "SC-Square: Future Progress with Machine Learning",

abstract = "The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production. In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.",

keywords = "machine learning, symbolic computation, computer algebra systems, satisfiability checking, SMT solvers",

author = "Matthew England",

note = "{\textcopyright} 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 6th International Workshop on Satisfiability Checking and Symbolic Computation : Bridging Two Communities to Solve Real Problems, SC2 Workshop 2021 ; Conference date: 19-08-2021 Through 20-08-2021",

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ER -

SC-Square: Future Progress with Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this