In this paper we study the proximal point algorithm (PPA) based predictioncorrection (PC) methods for monotone variational inequalities. Each iteration of these methods consists of a prediction and a correction. The p...In this paper we study the proximal point algorithm (PPA) based predictioncorrection (PC) methods for monotone variational inequalities. Each iteration of these methods consists of a prediction and a correction. The predictors are produced by inexact PPA steps. The new iterates are then updated by a correction using the PPA formula. We present two profit functions which serve two purposes: First we show that the profit functions are tight lower bounds of the improvements obtained in each iteration. Based on this conclusion we obtain the convergence inexactness restrictions for the prediction step. Second we show that the profit functions are quadratically dependent upon the step lengths, thus the optimal step lengths are obtained in the correction step. In the last part of the paper we compare the strengths of different methods based on their inexactness restrictions.展开更多
比较了汊点水位预测-校正法(junction-point water stage prediction and correction,JPWSPC)和经典的分级解法,在处理缓流河网汊点处回流效应时的不同,并对比了它们的计算效率。应用这2类方法时,Saint-Venant方程组都采用Preissmann格...比较了汊点水位预测-校正法(junction-point water stage prediction and correction,JPWSPC)和经典的分级解法,在处理缓流河网汊点处回流效应时的不同,并对比了它们的计算效率。应用这2类方法时,Saint-Venant方程组都采用Preissmann格式离散,生成的非线性离散方程用Newton-Raphson方法求解。比较表明:这2类方法都能处理普适河网,JPWSPC法无需求解整体连接矩阵,同时不会增加每一时间步的迭代次数,因而节约了系统内存,提高了计算效率;河网中河段数目越多,JPWSPC法的效率优势越明显。展开更多
基金The author was supported by NSFC Grant 10271054MOEC grant 20020284027 and Jiangsur NSF grant BK20002075.
文摘In this paper we study the proximal point algorithm (PPA) based predictioncorrection (PC) methods for monotone variational inequalities. Each iteration of these methods consists of a prediction and a correction. The predictors are produced by inexact PPA steps. The new iterates are then updated by a correction using the PPA formula. We present two profit functions which serve two purposes: First we show that the profit functions are tight lower bounds of the improvements obtained in each iteration. Based on this conclusion we obtain the convergence inexactness restrictions for the prediction step. Second we show that the profit functions are quadratically dependent upon the step lengths, thus the optimal step lengths are obtained in the correction step. In the last part of the paper we compare the strengths of different methods based on their inexactness restrictions.
文摘比较了汊点水位预测-校正法(junction-point water stage prediction and correction,JPWSPC)和经典的分级解法,在处理缓流河网汊点处回流效应时的不同,并对比了它们的计算效率。应用这2类方法时,Saint-Venant方程组都采用Preissmann格式离散,生成的非线性离散方程用Newton-Raphson方法求解。比较表明:这2类方法都能处理普适河网,JPWSPC法无需求解整体连接矩阵,同时不会增加每一时间步的迭代次数,因而节约了系统内存,提高了计算效率;河网中河段数目越多,JPWSPC法的效率优势越明显。
