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浙江大学学报（理学版）
浙江大学学报（理学版）
数学与计算机科学     
随机规划逼近解的收敛性
浙江大学数学系,浙江 杭州 310027
Convergence of approximate solutions in stochastic programming.
Department of Mathematics, Zhejiang University , Hangzhou 310027, China
 全文: PDF(159 KB)  
摘要： 本文对随机规划的逼近解的收敛性作了探讨,证明了当随机向量序列 {Y( k )(k) }依分布收敛于Y(k) 时, 相应于 Y( k )(k) 的随机规划问题的任何最优解序列将收敛到原问题的最优解, 这个结果对如何设计逼近算法提供了一个理论基础.
关键词： 随机规划;  依分布收敛;  逼近解    
Abstract: This paper studied the convergence of approximate solutions for stochastic programming and proved that any optimum solution sequence of corresponding problems will converge to one of the optimum solutions of the original problem if random vector sequence {Y( k )(k) } converges to Y(k) in distribution. These results provide the theoretical foundation for constructing approximate algorithms.
Key words: stochastic programming    convergence in distribution    approximate solution
出版日期: 2014-08-11
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骆建文
鲁世杰

引用本文:

骆建文,鲁世杰. 随机规划逼近解的收敛性[J]. 浙江大学学报（理学版）, .

LUO Jian-wen, LU Shi-jie. Convergence of approximate solutions in stochastic programming.. Journal of Zhejiang University (Science Edition), .

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https://www.zjujournals.com/sci/CN/    或    https://www.zjujournals.com/sci/CN/Y2000/V27/I5/493

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