Name
Abstract | ||
|---|---|---|
A challenging problem in computational mathematics is to compute roots of a high-degree univariate random polynomial. We combine an efficient multiprecision implementation for solving high-degree random polynomials with two certification methods, namely Smale's $\alpha$-theory and one based on Gerschgorin's theorem, for showing that a given numerical approximation is in the quadratic convergence region of Newton's method of some exact solution. With this combination, we can certifiably count the number of real roots of random polynomials. We quantify the difference between the two certification procedures and list the salient features of both of them. After benchmarking on random polynomials where the coefficients are drawn from the Gaussian distribution, we obtain novel experimental results for the Cauchy distribution case. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Discrete mathematics,Mathematical optimization,Random field,Classical orthogonal polynomials,Polynomial,Mathematical analysis,Cauchy distribution,Rate of convergence,Univariate,Difference polynomials,Mathematics,Random function |
DocType | Volume | Citations |
Journal | abs/1412.1717 | 1 |
PageRank | References | Authors |
0.37 | 4 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Cleveland | 1 | 1 | 0.37 |
| Jeffrey Dzugan | 2 | 1 | 0.37 |
| Jonathan D. Hauenstein | 3 | 269 | 37.65 |
| Ian Haywood | 4 | 8 | 0.88 |
| Dhagash Mehta | 5 | 15 | 8.26 |
| Anthony Morse | 6 | 3 | 1.07 |
| Leonardo Robol | 7 | 1 | 0.71 |
| Taylor Schlenk | 8 | 1 | 0.37 |