Optimization based on information containing uncertainties

Abstract

Considers the problem of finding extrema of an unknown function L(⋅):I R^l× R or the root set of its gradient f(⋅) ^{\underline \underline Δ} ▽ L(⋅) on the basis of observations, which may contain two kinds of uncertainties: random noise and structural uncertainties. The latter is caused by the fact that observations may not be made on the recent estimate, may not even be carried out on f(⋅) but on some other function. The optimization algorithm and conditions guaranteeing its convergence are given.

Citation

Chen, H. (2001), "Optimization based on information containing uncertainties", Kybernetes, Vol. 30 No. 9/10, pp. 1177-1183. https://doi.org/10.1108/EUM0000000006548