Gradient-based simulation optimization under probability constraints - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue European Journal of Operational Research Année : 2011

Gradient-based simulation optimization under probability constraints

Résumé

We study optimization problems subject to possible fatal failures. The probability of failure should not exceed a given confidence level. The distribution of the failure event is assumed unknown, but it can be generated via simulation or observation of historical data. Gradient-based simulation-optimization methods pose the difficulty of the estimation of the gradient of the probability constraint under no knowledge of the distribution. In this work we provide two single-path estimators with bias: a convolution method and a finite difference, and we provide a full analysis of convergence of the Arrow-Hurwicz algorithm, which we use as our solver for optimization. Convergence results are used to tune the parameters of the numerical algorithms in order to achieve best convergence rates, and numerical results are included via an example of application in finance.
Fichier non déposé

Dates et versions

hal-00676427 , version 1 (05-03-2012)

Identifiants

Citer

Laetitia Andrieu, Guy Cohen, Felisa Vázquez-Abad. Gradient-based simulation optimization under probability constraints. European Journal of Operational Research, 2011, 212 (2), pp.345-351. ⟨10.1016/j.ejor.2011.01.049⟩. ⟨hal-00676427⟩
100 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More