This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inerti...This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.展开更多
在体表标测系统中合理地选择合适的电极数量和位置,其临床应用的推广意义重大.为此,开发了一种由多导联体表记录选择最优导联位置的新算法.采用基于最小二乘法的多变量线性回归算法确定变换系数,将原始信号与重构势的空间残差均方值作...在体表标测系统中合理地选择合适的电极数量和位置,其临床应用的推广意义重大.为此,开发了一种由多导联体表记录选择最优导联位置的新算法.采用基于最小二乘法的多变量线性回归算法确定变换系数,将原始信号与重构势的空间残差均方值作为优化判据,分别用顺序前进法和前进-后退搜索法对奇异值分解(singular value decomposition,SVD)前后的体表势矩阵寻找最优导联组合.结果表明:所得到的4种最优导联位置不尽相同,但分区统计规律相同;同时,优选出的最优导联组合对测试样本的预测最大相对误差均在1%左右,表现出较好的适应性.展开更多
文摘This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.
文摘在体表标测系统中合理地选择合适的电极数量和位置,其临床应用的推广意义重大.为此,开发了一种由多导联体表记录选择最优导联位置的新算法.采用基于最小二乘法的多变量线性回归算法确定变换系数,将原始信号与重构势的空间残差均方值作为优化判据,分别用顺序前进法和前进-后退搜索法对奇异值分解(singular value decomposition,SVD)前后的体表势矩阵寻找最优导联组合.结果表明:所得到的4种最优导联位置不尽相同,但分区统计规律相同;同时,优选出的最优导联组合对测试样本的预测最大相对误差均在1%左右,表现出较好的适应性.