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The Conjugate Gradient Method olves Large Sparse Matrix Equations

机译:共轭梯度法OLVES大稀疏矩阵方程

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The Conjugate Gradient Method(CGM) is one of the methods to find extreme value solution of the poly-variable function. It has been proved that the problems to solve linear equations are equivalent to the problem to find extreme miniumum value of its quadratic equation. If the equation is linear, the true swolution can be get by finite steps of iteration theoretically. The merits of CGM are the storage capccity reduced and calculation ratio accelerated. In this papr, it is introduced that CGM is a suitable method for solving large sparse matrix equations, such as the 3-D electro-magnetic numeric calcualtion on the micro-computer.
机译:共轭梯度法(CGM)是找到多变量函数的极值解的方法之一。事实证明,解决线性方程的问题等同于找到其二次方程的极端初级值的问题。如果等式是线性的,则真正的速度可以通过理论上迭代的有限步骤来获得。 CGM的优点是储存CAPCCCITY减少和计算率加速。在该PAPR中,介绍CGM是用于求解大稀疏矩阵方程的合适方法,例如微计算机上的3-D电磁数字计算。

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