摘要
讨论了基于空间分解的随机二阶锥规划问题的样本均值近似方法(简称SAA方法),在适当的条件下,证明了SAA问题的解以概率1收敛到原问题的解,并且随着样本容量的增加收敛速度是指数的.基于分解理论,给出了SAA问题的超线性收敛算法框架.
Sample average approximation(SAA)method based on the space decomposition method to solve stochastic second-order cone programming problem is discussed. Under some moderate conditions,the SAA solution converges to its true counterpart with probability approaching one and convergence is exponential fast with the increase of sample size.Based on the decomposition theory,a superlinear convergent algorithm frame is designed to solve the SAA problem.
出处
《沈阳大学学报（自然科学版）》
CAS
2016年第3期250-255,共6页
Journal of Shenyang University：Natural Science
基金
国家自然科学基金资助项目(11301347)
关键词
随机优化
二阶锥规划
样本均值近似
空间分解
超线性收敛
stochastic optimization
second-order cone programming
sample average approximation
space decomposition
superlinear convergent
