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The classic iterative ones need special characters of the coefficient matrix; CG has over linear convergence, but the coefficient matrix is positive definite; Krylov subspace ones can deal with non-symmetric linear systems, but sometimes it must face"collapse"problems.

经典迭代法的收敛需要由系数矩阵的一些特定性质来保证;共轭梯度法具有超线性收敛性,但是系数矩阵必须是正定矩阵;Krvlov子空间法可用于非对称矩阵问题,但可能出现崩溃现象。

In view of convergence of iterative arithmetic, the arithmetic of selfadjusting the linear search directions during iteration was put forward based on practice, which guaranteed fast convergence and robustness of the system.

5对于Newton-Rapson迭代过程,根据实践,提出自动控制和调节收敛因子的算法,使系统具有较高的收敛性和计算稳定性。

The global convergence and local superlinear rate are obtained under certain conditions.

在一般信赖域方法的假摘要设条件下,我们证明了该方法的全局收敛性及局部收敛速度。

Li Xiangru found an error in Comuniciu's proof of Mean Shift convergence, and gave an indirect condition for its convergence.

李乡儒指出了Comuniciu关于算法收敛性证明中的错误,并给出了一个算法收敛的间接条件。

However, in many cases, one must give rather strong conditions, such as the strong monotonicity, to ensure the convergence of the method.

不过,在许多情形下,该算法的收敛需要较强的条件,诸如强收敛性等。

The monotonicity and the convergence of deterministic nature are proved.

文中给出了误差函数的单调性定理以及该算法的弱收敛和强收敛性定理。

The saw-toothed geometric boundary of Cartesian Grid has bad influences to the simulation convergence. However, under the circumstances that grid is smooth (i.e. without the saw-tooth boundary), the simulation converges better as the grid level increases.

卡氏网格在几何边界所形成之锯齿状边界对於模拟计算之收敛性有不良的影响,然而,在边界平滑无锯齿状的情形下,卡氏网格层数越多,程式收敛情形越佳。

In the paper, the methods above are studied for element solve singular problems the convergence of the method is proved and the error estimate is also obtained.

本文将讨论零空间为一维情况下Chebyshev 方法求解奇异问题的收敛性,得到了相应的渐近收敛速率。4。

Puthermore,we make a comparison between its optimum spectral radius and that of SOR method and draw a conclusion that these two methods have their own advantages respectively under different conditions.

除了对迭代方法的研究和改进,对方程组本身做某些处理,如对它进行预条件也是有效改善迭代收敛性,加快迭代收敛速度的方法之一。

Ensure global convergence of random PSO algorithm for high-dimensional functions is a strong global convergence.

保证全局收敛的随机粒子群优化算法,对于高维函数有较强的全局收敛性。

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