Convex Hull Abstraction in Specialisation of CLP Programs

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    We introduce an abstract domain consisting of atomic formulas constrained by linear arithmetic constraints (or convex hulls). This domain is used in an algorithm for specialization of constraint logic programs. The algorithm incorporates in a single phase both top-down goal directed propagation and bottom-up answer propagation, and uses a widening on the convex hull domain to ensure termination. We give examples to show the precision gained by this approach over other methods in the literature for specializing constraint logic programs. The specialization method can also be used for ordinary logic programs containing arithmetic, as well as constraint logic programs. Assignments, inequalities and equalities with arithmetic expressions can be interpreted as constraints during specialization, thus increasing the amount of specialization that can be achieved.
    Original languageEnglish
    Book seriesLecture Notes in Computer Science
    Issue number2664
    Pages (from-to)90-108
    Number of pages19
    ISSN0302-9743
    Publication statusPublished - 2003

    Cite this

    @article{c651fc8052bd11dba4bc000ea68e967b,
    title = "Convex Hull Abstraction in Specialisation of CLP Programs",
    abstract = "We introduce an abstract domain consisting of atomic formulas constrained by linear arithmetic constraints (or convex hulls). This domain is used in an algorithm for specialization of constraint logic programs. The algorithm incorporates in a single phase both top-down goal directed propagation and bottom-up answer propagation, and uses a widening on the convex hull domain to ensure termination. We give examples to show the precision gained by this approach over other methods in the literature for specializing constraint logic programs. The specialization method can also be used for ordinary logic programs containing arithmetic, as well as constraint logic programs. Assignments, inequalities and equalities with arithmetic expressions can be interpreted as constraints during specialization, thus increasing the amount of specialization that can be achieved.",
    author = "J.C. Peralta and Gallagher, {John Patrick}",
    year = "2003",
    language = "English",
    pages = "90--108",
    journal = "Lecture Notes in Computer Science",
    issn = "0302-9743",
    publisher = "Physica-Verlag",
    number = "2664",

    }

    Convex Hull Abstraction in Specialisation of CLP Programs. / Peralta, J.C.; Gallagher, John Patrick.

    In: Lecture Notes in Computer Science, No. 2664, 2003, p. 90-108.

    Research output: Contribution to journalJournal articleResearchpeer-review

    TY - JOUR

    T1 - Convex Hull Abstraction in Specialisation of CLP Programs

    AU - Peralta, J.C.

    AU - Gallagher, John Patrick

    PY - 2003

    Y1 - 2003

    N2 - We introduce an abstract domain consisting of atomic formulas constrained by linear arithmetic constraints (or convex hulls). This domain is used in an algorithm for specialization of constraint logic programs. The algorithm incorporates in a single phase both top-down goal directed propagation and bottom-up answer propagation, and uses a widening on the convex hull domain to ensure termination. We give examples to show the precision gained by this approach over other methods in the literature for specializing constraint logic programs. The specialization method can also be used for ordinary logic programs containing arithmetic, as well as constraint logic programs. Assignments, inequalities and equalities with arithmetic expressions can be interpreted as constraints during specialization, thus increasing the amount of specialization that can be achieved.

    AB - We introduce an abstract domain consisting of atomic formulas constrained by linear arithmetic constraints (or convex hulls). This domain is used in an algorithm for specialization of constraint logic programs. The algorithm incorporates in a single phase both top-down goal directed propagation and bottom-up answer propagation, and uses a widening on the convex hull domain to ensure termination. We give examples to show the precision gained by this approach over other methods in the literature for specializing constraint logic programs. The specialization method can also be used for ordinary logic programs containing arithmetic, as well as constraint logic programs. Assignments, inequalities and equalities with arithmetic expressions can be interpreted as constraints during specialization, thus increasing the amount of specialization that can be achieved.

    M3 - Journal article

    SP - 90

    EP - 108

    JO - Lecture Notes in Computer Science

    JF - Lecture Notes in Computer Science

    SN - 0302-9743

    IS - 2664

    ER -