共轭梯度法

词组短语

conjugate gradient method · method of conjugate gradients

与共轭梯度法相关的网络例句 [注：此内容来源于网络，仅供参考]

Combined CD method and a new conjugate gradient method given by Liu.Y, Storey. C, the second class of nonlinear conjugate gradient method is proposed, which not only has descent property, but also is proved global convergence with the general Wolfe line search. Finally, the numerical results show this class of conjugate gradient methods is very efficient.

第二类算法是结合CD法和Liu.Y， Storey.C提出的新共轭梯度法，提出一类新的非线性共轭梯度法，新方法不但具有下降性质，而且在推广的Wolfe线搜索下是全局收敛的，最后进行了数值验证。

The content of our curriculum are:Optimization under unconstrained conditions and constrained conditions,we'll focus on common methods of this field like Conjugate gradient method,DFP,POWELL method,The multiplier method,Penalty Function Method and so on.

主要讲授内容为无约束条件及有约束条件下的优化设计，重点介绍了共轭梯度法，变尺度法，POWELL法，乘子法，惩罚函数法等电路优化的常用方法。

The definition of generalized conjugate (or M-conjugate) is introduced and the generalized conjugate gradient method is presented.

引入了广义共轭的概念，在此基础上提出了广义共轭梯度法。

Meanwhile the biconjugate gradient method instead of the conjugate gradient method is used to accelerate the iteration process.

同时用双共轭梯度法代替共轭梯度法来加速迭代过程。

The reconstruction problem is ill-posed, so two optimal criterions, the least module and the smoothness criterion base on Tikhonov regularization technique, are introduced into reconstruction algorithm. Many regularization parameters choice strategies are investigated, and the TPA(Two—Parameter Algorithm) strategy which is based on the Morozov discrepancy principles, is implemented in two regularization reconstruction algorithms.Numerical experiment results show that the nonnegative and smoothness constraint condition can overcome the difficulty of iteration semiconvergent, preconditioned technique can improve convergence rate and reconstruction accuracy, smoothness regularization criterion can meliorate ill-posed problem of reconstruction and enhance iteration stability, and the TPA is an effective strategy of regularization parameters choice.

数值试验表明：在共轭梯度法中引入非负约束和光滑约束改善了迭代的"半收敛"性，非负约束保证了解的非负性，光滑约束抑制了重建解的振荡现象，约束算法的重建精度与无约束算法相比大幅度提高；在约束共轭梯度重建算法中引入预优技术，可以加快算法的收敛速度，提高迭代的稳定性和重建精度；引入光滑准则的正则化技术可以有效改善图像重建问题的不适定性，加快迭代的收敛速度，提高迭代的稳定性和图像重建质量，计算正则参数的TPA算法在闪光照相图像重建中是有效的。

Consequently, when applied to minimize a strictly convex quadratic function, the proposed methods terminate at the solution of the problem finitely.

因此，当目标函数是严格凸的二次函数，且采用精确线性搜索时，这些修正的共轭梯度法具有共轭性和二次终止性。

First, based on quadratic model and Perrys conjugacy condition, two new formulas of the main parameter β_k of conjugate gradient method are proposed.

第一，本文基于二次模型及Perry提出的共轭性条件，导出共轭梯度法的主要参数β_k的两种新形式。

With the guide of non-linear program theory, by using interpolative steps and inexact line search, an improved conjugate gradient method was found, by which the training rate of BP networks increases by tens or hundreds of times. Moreover, the improved method is effective to solve non-linear equations for which Newton's method does not converge owing to the problems of the quadratic derivative and inverse matrix.

通过大量的数值模拟试验发现，在非线性规划理论的指导下采用间插步骤和不精确的一维搜索技术改进的共轭梯度法，是基于梯度和共轭方向的连续搜索算法中最有效的算法，这种算法使BP网络的训练速度提高几十到几百倍，使BP网络的实际应用效果大为改善；而且这种算法对于用牛顿法由于求二阶导数和求逆矩阵等问题难于收敛的非线性方程组的求解也是很有效的。

Chapter 8: We discuss the acceleration of multigrid by Krylov subspace approaches, and recognize the reason for slow convergence of algebraic multigrid methods is that error reduction is significantly less efficient for some very specific error components which may cause a few eigenvalues of the algebraic multigrid iteration matrix to be considerably closer to 1 than all the rest. However, the eigenvectors belonging to the few isolated eigenvalues can be expected to be typically captured after only a few conjugate gradient iterations, which accelerate algebraic multigrid algorithms. Theoretical analysis and numerical results of some practical problems show the iterant recombination accelerates algebraic multigrid convergence.

第八章：先介绍用Krylov子空间迭代法加速一般多重网格方法收敛的基本框架，然后紧紧抓住引起代数多重网格方法收敛减慢的根本原因往往是误差减小对几个特别的误差分量不明显，这导致代数多重网格方法的迭代矩阵的几个特征值接近于1，而共轭梯度方法则能比较典型地捕捉属于孤立特征值的特征向量，从而推导出有效的共轭梯度加速算法和程序实现，不仅从一些具体的实际应用例子的数值结果去验证迭代复合加速收敛的效果，而且还从理论上分析了预处理共轭梯度法加速代数多重网格法收敛的机理。

The reconstruction problem is ill-posed, so two optimal criterions, the least module and the smoothness criterion base on Tikhonov regularization technique, are introduced into reconstruction algorithm. Many regularization parameters choice strategies are investigated, and the TPA(Two—Parameter Algorithm) strategy which is based on the Morozov discrepancy principles, is implemented in two regularization reconstruction algorithms.Numerical experiment results show that the nonnegative and smoothness constraint condition can overcome the difficulty of iteration semiconvergent, preconditioned technique can improve convergence rate and reconstruction accuracy, smoothness regularization criterion can meliorate ill-posed problem of reconstruction and enhance iteration stability, and the TPA is an effective strategy of regularization parameters choice.

数值试验表明：在共轭梯度法中引入非负约束和光滑约束改善了迭代的&半收敛&性，非负约束保证了解的非负性，光滑约束抑制了重建解的振荡现象，约束算法的重建精度与无约束算法相比大幅度提高；在约束共轭梯度重建算法中引入预优技术，可以加快算法的收敛速度，提高迭代的稳定性和重建精度；引入光滑准则的正则化技术可以有效改善图像重建问题的不适定性，加快迭代的收敛速度，提高迭代的稳定性和图像重建质量，计算正则参数的TPA算法在闪光照相图像重建中是有效的。

与共轭梯度法相关的网络解释 [注：此内容来源于网络，仅供参考]

conjugate gradient method：共轭梯度法

共轭梯度法(Conjugate Gradient Method)是以共轭方向(Conjugate Direction)作为搜索方向的一类算法. 最初的共轭梯度法由Hesteness和Stiefel于1952年为求解线性方程组而提出,后来用于求解无约束最优化问题.

conjugate gradient method：共轭梯度法,共轭斜量法

2700. conjugate functions 共轭函数 | 2701. conjugate gradient method 共轭梯度法,共轭斜量法 | 2702. conjugate image 共轭象

three-term conjugate gradient method：三项共轭梯度法

预条件共扼梯度法:Preconditioned Conjugate Gradient Method | 三项共轭梯度法:three-term conjugate gradient method | 非线性共轭梯度法:nonlinear conjugate gradients method

Preconditioned Conjugate Gradient Method：预条件共轭梯度法

共扼梯度最优化:Conjugate Gradient Optimization | 预条件共轭梯度法:preconditioned conjugate gradient method | 多搜索方向共扼梯度方法:multiple search directions conjugate gradient

nonlinear conjugate gradient method：非线性共轭梯度法

针织机三角优化设设:the Optimum Design of Nonlinear Cam | 非线性共轭梯度法:nonlinear conjugate gradient method | 非线性有限单元法:Nonlinear Finite Element Method.

nonmonotone conjugate gradient method：非单调共轭梯度法

条件预优共轭梯度法:Pre- conditioned conjugate gradient method | 非单调共轭梯度法:nonmonotone conjugate gradient method | 三项共轭梯度算法:three-term conjugate gradient method

Konjugierte-Gradienten-Verfahren Conjugate Gradient Method：共轭梯度法

konjugierte Untergruppe conjugate subgroup 共轭子群 | Konjugierte-Gradienten-Verfahren Conjugate Gradient Method 共轭梯度法 | Konjunktion conjunction 合取

method of conjugate gradients：共轭梯度法

method of comparison 比较法 | method of conjugate gradients 共轭梯度法 | method of difference 差分法

biconjugate gradient method：双共轭梯度法

共轭梯度方法:conjugate gradient method | 双共轭梯度法:biconjugate gradient method | 温度梯度法:thermal gradient method

SSOR preconditioned CG method：对称超松弛预处理共轭梯度法

梯度法:Gradient-based method | 对称超松弛预处理共轭梯度法:SSOR preconditioned CG method | 三参数共轭梯度法簇:A family of three parameter conjugate gradient medhod