Recent Developments in Real Quantifier Elimination and Cylindrical Algebraic Decomposition (Extended Abstract of Invited Talk)

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Abstract

This extended abstract accompanies an invited talk at CASC 2024, which surveys recent developments in Real Quantifier Elimination (QE) and Cylindrical Algebraic Decomposition (CAD). After introducing these concepts we will first consider adaptations of CAD inspired by computational logic, in particular the algorithms which underpin modern SAT solvers. CAD theory has found use in collaboration with these via the Satisfiability Modulo Theory (SMT) paradigm; while the ideas behind SAT/SMT have led to new algorithms for Real QE. Second we will consider the optimisation of CAD through the use of Machine Learning (ML). The choice of CAD variable ordering has become a key case study for the use of ML to tune algorithms in computer algebra. We will also consider how explainable AI techniques might give insight for improved computer algebra software without any reliance on ML in the final code.
Original languageEnglish
Title of host publicationComputer Algebra in Scientific Computation
EditorsFrançois Boulier, Chenqi Mou, Timur M. Sadykov, Evgenii V. Vorozhtsov
PublisherSpringer
Chapter1
Pages1-10
Number of pages10
Volume14938
ISBN (Electronic)978-3-031-69070-9
ISBN (Print)978-3-031-69069-3
DOIs
Publication statusPublished - 21 Aug 2024
Event26th International Computer Algebra in Scientific Computing - Rennes, France
Duration: 2 Sept 20246 Sept 2024
https://casc-conference.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Computer Algebra in Scientific Computing
Abbreviated titleCASC 2024
Country/TerritoryFrance
CityRennes
Period2/09/246/09/24
Internet address

Funding

The author is currently supported by UKRI EPSRC grant EP/T015748/1: Pushing Back the Doubly-Exponential Wall of Cylindrical Algebraic Decomposition (the DEWCAD Project). Some of the work surveyed was supported by UKRI EPSRC grant EP/R019622/1, Embedding Machine Learning within Quantifier Elimination Procedures; and EU H2020 FET CSA 712689, Satisfiability Checking and Symbolic Computation (the SC Project).

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/T015748/1, EP/R019622/1
European Horizon 2020FET CSA 712689

Keywords

  • Quantifier elimination
  • Cylindrical algebraic decomposition
  • SAT
  • SMT
  • Machine learning
  • Explainable AI

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