Per citar aquest document:
A Gaussian upper bound for Gaussian multi-stage stochastic linear programs
Schweitzer, Eithan
Avriel, Mordecai

Data: 1997
Resum: This paper deals with two-stage and multi-stage stochastic programs in which the right-hand sides of the constraints are Gaussian random variables. Such problems are of interest since the use of Gaussian estimators of random variables is widespread. We introduce algorithms to find upper bounds on the optimal value of two-stage and multi-stage stochastic (minimization) programs with Gaussian right-hand sides. The upper bounds are obtained by solving deterministic mathematical programming problems with dimensions that do not depend on the sample space size. The algorithm for the two-stage problem involves the solution of a deterministic linear program and a simple semidefinite program. The algorithm for the multi-stage problem involves the solution of a quadratically constrained convex programming problem.
Drets: Tots els drets reservats
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Stochastic programming ; Semidefinite programming ; Multi-stage linear programs
Publicat a: Mathematical Programming, vol. 77 n. 1 (1997) p. 1-21, ISSN 0025-5610

21 p, 954.9 KB
 Accés restringit a la UAB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2006-03-13, darrera modificació el 2017-09-29

   Favorit i Compartir