Confidence intervals for the minimum of a function using extreme value statistics Online publication date: Thu, 26-Mar-2015
by Miguel De Carvalho
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 2, No. 3, 2011
Abstract: Stochastic search algorithms are becoming an increasingly popular tool in the optimisation community. The random structure of these methods allows us to sample from the range of a function and to obtain estimates of its global minimum. However, a major advantage of stochastic search algorithms over deterministic algorithms, which is frequently unexplored, is that they also allow us to obtain interval estimates. In this paper, we put forward such advantage by providing guidance on how to combine stochastic search and optimisation methods with extreme value theory. To illustrate this approach we use several well-known objective functions. The obtained results are encouraging, suggesting that the interval estimates yield by this approach can be helpful for supplementing point estimates produced by other sophisticated optimisation methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com