Solution of Stochastic Quadratic Programming with Imperfect Probability Distribution Using Nelder-Mead Simplex Method

Autor:	Xin Liu, Xinshun Ma
Rok vydání:	2018
Předmět:	Simplex algorithm Complete information Linear partial information MathematicsofComputing_NUMERICALANALYSIS Applied mathematics Probability distribution Field (mathematics) Quadratic programming Nelder–Mead method Random variable Mathematics
Zdroj:	Journal of Applied Mathematics and Physics. :1111-1119
ISSN:	2327-4379 2327-4352
DOI:	10.4236/jamp.2018.65095
Popis:	Stochastic quadratic programming with recourse is one of the most important topics in the field of optimization. It is usually assumed that the probability distribution of random variables has complete information, but only part of the information can be obtained in practical situation. In this paper, we propose a stochastic quadratic programming with imperfect probability distribution based on the linear partial information (LPI) theory. A direct optimizing algorithm based on Nelder-Mead simplex method is proposed for solving the problem. Finally, a numerical example is given to demonstrate the efficiency of the algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d7f15aaaaa7a44d7120aa6a3f18fe3a6 https://doi.org/10.4236/jamp.2018.65095 Zobrazit plný text záznamu