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Probabilistic Robustness Analysis -- Risks, Complexity and Algorithms | | Xinjia Chen
; Kemin Zhou
; Jorge L. Aravena
; | | Date: |
5 Jul 2007 | | Abstract: | It is becoming increasingly apparent that probabilistic approaches can
overcome conservatism and computational complexity of the classical worst-case
deterministic framework and may lead to designs that are actually safer. In
this paper we argue that a comprehensive probabilistic robustness analysis
requires a detailed evaluation of the robustness function and we show that such
evaluation can be performed with essentially any desired accuracy and
confidence using algorithms with complexity linear in the dimension of the
uncertainty space. Moreover, we show that the average memory requirements of
such algorithms are absolutely bounded and well within the capabilities of
today’s computers.
In addition to efficiency, our approach permits control over statistical
sampling error and the error due to discretization of the uncertainty radius.
For a specific level of tolerance of the discretization error, our techniques
provide an efficiency improvement upon conventional methods which is inversely
proportional to the accuracy level; i.e., our algorithms get better as the
demands for accuracy increase. | | Source: | arXiv, arxiv.0707.0828 | | Services: | Forum | Review | PDF | Favorites | |
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