Name
Abstract | ||
|---|---|---|
This paper addresses the issues of conservativeness and computational complexity of probabilistic robustness analysis. The
authors solve both issues by defining a new sampling strategy and robustness measure. The new measure is shown to be much
less conservative than the existing one. The new sampling strategy enables the definition of efficient hierarchical sample
reuse algorithms that reduce significantly the computational complexity and make it independent of the dimension of the uncertainty
space. Moreover, the authors show that there exists a one to one correspondence between the new and the existing robustness
measures and provide a computationally simple algorithm to derive one from the other. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11424-009-9144-z | J. Systems Science & Complexity |
Keywords | Field | DocType |
robustness analysis,uncertain system,risk analysis,uncertain system.,computational complexity,robustness analysjs,randomized algorithms,randomized algorithm | Randomized algorithm,Mathematical optimization,Reuse,Computer science,Probabilistic analysis of algorithms,Robustness (computer science),Sampling (statistics),SIMPLE algorithm,Probabilistic logic,Computational complexity theory | Journal |
Volume | Issue | ISSN |
22 | 1 | 1559-7067 |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinjia Chen | 1 | 47 | 12.21 |
| Kemin Zhou | 2 | 372 | 59.31 |
| Jorge Aravena | 3 | 4 | 1.11 |