Linear programming approximations for Markov control processes in metric spaces

被引：13
作者：
Hernandez-Lerma, O
Lasserre, JB
机构：
[1] Inst Politecn Nacl, Ctr Invest & Estudios Avanzados, Dept Matemat, Mexico City 07000, DF, Mexico
[2] CNRS, LAAS, F-31077 Toulouse, France
来源：
ACTA APPLICANDAE MATHEMATICAE | 1998年 / 51卷 / 02期
关键词：
(discrete-time) Markov control processes; infinite-dimensional linear programming; aggregation; relaxation; inner approximations;
D O I：
10.1023/A:1005826226226
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We develop a general framework to analyze the convergence of linear-programming approximations for Markov control processes in metric spaces. The approximations are based on aggregation and relaxation of constraints, as well as inner approximations of the decision variables. In particular, conditions are given under which the control problem's optimal value can be approximated by a sequence of finite-dimensional linear programs.
引用
收藏
页码：123 / 139
页数：17
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