一类新的带记忆模型的非单调信赖域算法  

A Nonmonotonic Trust Region Algorithm with a Memory Model for Unconstrained Optimization

作　　者：易存晓[1] 胡永才[1] 

机构地区：[1]河南工业职业技术学院数学系,河南南阳473009

出　　处：《计算机工程与科学》2008年第2期70-71,150,共3页Computer Engineering & Science

摘　　要：本文就无约束优化问题提出了一个带记忆模型的非单调信赖域算法。与传统的非单调信赖域算法不同,文中的信赖域子问题的逼近模型为记忆模型,该模型使我们可以从更全面的角度来求得信赖域试探步,从而避免了传统非单调信赖域方法中试探步的求取完全依赖于当前点的信息而过于局部化的困难。文中提出了一个带记忆模型的非单调信赖域算法,并证明了其全局收敛性。In this paper, we develop a nonmonotonic trust region algorithm for unconstrained optimization. Different from the traditional nonmonotonic trust region algorithms, this algorithm includes a memory model, which makes the algorithm more farsighted in the sense that its behavior is not completely dominated by the local nature of the objective function. We present a nonmonotonic trust region algorithm that has this feature, and prove its global convergenc under suitable conditions.

关 键 词：无约束优化 记忆模型 非单调信赖域算法 全局收敛性 

分 类 号：O221.2[理学—运筹学与控制论]