{"type":"ScholarlyArticle","title":"An Approximate Solver for Symbolic Equations","authors":[{"name":"Elisha Sacks","slug":"elisha-sacks","given":"Elisha","family":"Sacks","affiliation":""}],"abstract":"This paper describes a program, called NEWTON, that finds approximate symbolic solutions to parameterized equations in one variable. NEWTON derives an initial approximation by solving for the dominant term in the equation, or if this fails, by bisection. It refines this approximation by a symbolic version of Newton's method. It tests whether the first Newton iterate lies closer to the solution than does the initial solution. If so, it returns this iterate; otherwise, it chooses a new initial solution and tries again.","doi":null,"url":"https://ijcai.org/proceedings/1989/69","pdf":"https://ijcai.org/proceedings/1989/0069.pdf","markdown":"https://ijcai.org/proceedings/1989/69.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/69","volume":{"slug":"1989","title":"Proceedings of the Eleventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1989","url":"https://ijcai.org/proceedings/1989/","year":1989,"location":"Detroit, Michigan, USA","dates":"August 20-25 1989","editors":"N. S. Sridharan","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Automated Deduction","track":"Automated Deduction","number":69,"pages":{"first":431,"last":433},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}