Name
Abstract | ||
|---|---|---|
There are many problems with the simplification of elementary functions, particularly over the complex plane. Systems tend to make major errors, or not to simplify enough. In this paper we outline the "unwinding number" approach to such problems, and show how it can be used to prevent errors and to systematise such simplification, even though we have not yet reduced the simplification process to a complete algorithm. The unsolved problems are probably more amenable to the techniques of artificial intelligence and theorem proving than the original problem of complex-variable analysis. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-44990-6_9 | AISC |
Keywords | Field | DocType |
complex-variable analysis,artificial intelligence,complex plane,simplification process,major error,complete algorithm,unsolved problem,original problem,elementary functions,elementary function,complex analysis,computer algebra,artificial intelligent,theorem proving | Text simplification,Computer science,Elementary function,Automated theorem proving,Symbolic computation,Theoretical computer science,Complex plane,Artificial intelligence,Calculus,Multivalued function | Conference |
Volume | ISSN | ISBN |
1930 | 0302-9743 | 3-540-42071-1 |
Citations | PageRank | References |
8 | 0.92 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert M. Corless | 1 | 1239 | 127.79 |
| James H. Davenport | 2 | 844 | 141.40 |
| David J. Jeffrey | 3 | 1172 | 132.12 |
| Gurjeet Litt | 4 | 8 | 0.92 |
| Stephen M. Watt | 5 | 671 | 84.72 |