We solve the inverse problem using the conjugate gradient (CG) method, using Akaike's Information Criterion (AIC) aic to truncate the CG expansion.
通过应用共轭梯度法来解决反演问题，应用 赤池弘次的AIC信息准则来截短 共轭梯度扩大。
Onthe other hand, with traditional iterations and the conjugate gradient(CG) as smoothers, we can show the optimal convergence rate of the cascadic method in energy norm for 1-D and 2-D cases.
另一方面，采用传统迭代子和共轭梯度法作为光滑子，我们证明了瀑布型多重网格法对一、二维非线性椭圆边值问题，在能量范数下，均可获得最优收敛阶。
Onthe other hand, with traditional iterations and the conjugate gradient(CG) as smoothers, we can show the optimal convergence rate of the cascadic method in energy norm for 1-D and 2-D cases.
另一方面，采用传统迭代子和共轭梯度法作为光滑子，我们证明了瀑布型多重网格法对一、二维非线性椭圆边值问题，在能量范数下，均可获得最优收敛阶。
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