Name
Abstract | ||
|---|---|---|
This paper addresses the problem of computing the worst-case expected value of a polynomial function, over a class of admissible distributions. It is shown that this problem, for the class of distributions considered, is equivalent to a convex optimization problem for which efficient linear matrix inequality (LMI) relaxations are available. In case that the performance function is continuous (not necessarily polynomial), the worst-case expected value can be approximated by using its polynomial approximations. Moreover, the proposed approach is applied to compute hard bounds of the worst-case probability of a polynomial being negative. Numerical examples are presented which illustrate the application of the results in this paper. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CDC.2009.5399737 | CDC |
Keywords | Field | DocType |
statistical distributions,polynomial uncertainty,linear matrix inequality,convex programming,distributional robustness analysis,polynomial approximation,worst case probability,linear matrix inequalities,convex optimization,polynomials,expected value,manganese,data mining,optimization,monte carlo methods,probability density function | Characteristic polynomial,Stable polynomial,Mathematical optimization,Polynomial,Kharitonov's theorem,Monic polynomial,Reciprocal polynomial,Matrix polynomial,Wilkinson's polynomial,Mathematics | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3872-3 | 978-1-4244-3872-3 | 4 |
PageRank | References | Authors |
0.43 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Feng | 1 | 63 | 15.02 |
| Constantino M. Lagoa | 2 | 164 | 25.38 |