---
title: "An Approximate Solver for Symbolic Equations"
authors:
  - "Elisha Sacks"
doi: ""
url: "https://ijcai.org/proceedings/1989/69"
pdf: "https://ijcai.org/proceedings/1989/0069.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "431-433"
track: "Automated Deduction"
keywords:
---

# An Approximate Solver for Symbolic Equations

Elisha Sacks

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 431-433.

## 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.