Description: 上述算法的终止准则为H终止准则。要求编写通用程序。
关于函数的要求：
直线搜索所计算的函数自己任选。
共轭梯度法所计算的函数：①计算一个正定二次函数（至少是4元函数）；②至少计算一个非二次函数（至少是5元函数）。
非线性最小二乘问题的修正Gauss-Newton法所计算的函数：至少计算一个非线性函数（至少是5元函数）。
乘子法所计算的问题：等式约束、不等式约束要求至少各有一个。问题可在教材或其它参考书中任意选取。
程序自行编写（禁止采用调用现成软件的方式），编程语言自选。
-above algorithm criteria for the termination of H termination criteria. Requests for common procedures. The demands on function : linear search function by calculating their options. The conjugate gradient method by calculating function : calculate a definite quadratic function (at least four yuan function); calculated at least one non-quadratic function (at least five yuan functions). The nonlinear least squares problems that Gauss-Newton method by calculating function : at least a nonlinear function computing (at least five yuan functions). Multiplier Method calculation problem : identity bound inequality constraints have required at least one. Problems in reference books or other materials were selected at random. Self-preparation procedure (called ban on the use of existing software) Platform: |
Size: 9632 |
Author:洪男 |
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Description: 上述算法的终止准则为H终止准则。要求编写通用程序。
关于函数的要求：
直线搜索所计算的函数自己任选。
共轭梯度法所计算的函数：①计算一个正定二次函数（至少是4元函数）；②至少计算一个非二次函数（至少是5元函数）。
非线性最小二乘问题的修正Gauss-Newton法所计算的函数：至少计算一个非线性函数（至少是5元函数）。
乘子法所计算的问题：等式约束、不等式约束要求至少各有一个。问题可在教材或其它参考书中任意选取。
程序自行编写（禁止采用调用现成软件的方式），编程语言自选。
-above algorithm criteria for the termination of H termination criteria. Requests for common procedures. The demands on function : linear search function by calculating their options. The conjugate gradient method by calculating function : calculate a definite quadratic function (at least four yuan function); calculated at least one non-quadratic function (at least five yuan functions). The nonlinear least squares problems that Gauss-Newton method by calculating function : at least a nonlinear function computing (at least five yuan functions). Multiplier Method calculation problem : identity bound inequality constraints have required at least one. Problems in reference books or other materials were selected at random. Self-preparation procedure (called ban on the use of existing software) Platform: |
Size: 9216 |
Author:洪男 |
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Description: 图像重建常常被转化为解非线性无约束极值问题, 通过范数极小化推导出共扼梯度法的
一般算法。通过对模拟数据和实际工件断层扫描数据进行图像重建, 估计了算法的有效性, 结果表明, 与最速下降法相比, 此算法更适用于不完全投影数据的图像重建, 在保证重建图像拟合度的同时, 大大提高了重建速度。-Image reconstruction has often been transformed into solving nonlinear unconstrained extremum problem, through the norm minimization derived conjugate gradient method the general algorithm. Through the simulation data and actual data workpiece tomography image reconstruction, it is estimated that the effectiveness of the algorithm, the results showed that compared to steepest descent method, this algorithm is more applicable to incomplete projection data image reconstruction, in ensuring that the reconstructed image fit at the same time, greatly improving the speed of reconstruction. Platform: |
Size: 364544 |
Author:孙翔 |
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Description: 我编写的一个关于解决非线性方程的优化问题的共轭梯度法，希望对大家有用-I prepared a solution of nonlinear equations about the optimization problem of conjugate gradient method, in the hope that useful Platform: |
Size: 18432 |
Author:xujun |
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Description: 用MATLAB求解无约束的问题，主要有最速下降法，牛顿法，共轭梯度法，变尺度法（DFP和BFGS法），非线性最小二乘法。
用MATLAB求解有约束的问题，主要是外惩罚函数和广义乘子法。
以及一些对具体问题的分析，MATLAB的代码在文档里都有。
-Using MATLAB to solve the problem of non-binding, there are the steepest descent method, Newton method, conjugate gradient method, variable metric method (DFP and BFGS method), nonlinear least square method. Using MATLAB to solve the problem of binding, mainly outside the penalty function method and generalized multipliers. As well as some specific issues for analysis, MATLAB code in the document, are limitless. Platform: |
Size: 81920 |
Author:ljw |
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Description: matlab编写的共轭梯度法，用来解决非线性优化问题-matlab prepared by the conjugate gradient method is used to solve nonlinear optimization problems Platform: |
Size: 9216 |
Author:zh |
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Description: 各种求解非线性方程组的算法程序，包括牛顿法及变形，不动点迭代，共轭梯度-Various algorithms for solving nonlinear equations procedures, including Newton and deformation, fixed point iteration, conjugate gradient, etc., etc. Platform: |
Size: 11264 |
Author:廖益文 |
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Description: 共轭梯度法是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。-Conjugate gradient method is between the steepest descent method and Newton method between a method that only use the first derivative information, but the steepest descent method to overcome the disadvantage of slow convergence, but also avoids the need to store and calculate Newton Hesse matrix and the shortcomings of the inverse, conjugate gradient method is not only linear equations to solve large-scale one of the most useful, large-scale nonlinear optimization solution is also the most efficient algorithms. Platform: |
Size: 1024 |
Author:sunling |
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Description: 该软件包CG +是为解决大规模，无约束，非线性优化问题的共轭梯度代码。企业管治+实现了共轭梯度法的三个不同版本：弗莱彻-里夫斯方法，波拉克Ribiere方法的，积极的波拉克-Ribiere方法的（聊天总是非负）-The package CG+ is a Conjugate Gradient code for solving large-scale, unconstrained, nonlinear optimization problems. CG+ implements three different versions of the Conjugate Gradient method: the Fletcher-Reeves method, the Polak-Ribiere method, and the positive Polak-Ribiere method (Beta always non-negative). Platform: |
Size: 14336 |
Author:zjworlder |
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Description: 共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。-Conjugate gradient method (Conjugate Gradient) between the steepest descent between law and Newton' s method is a method, it is only the first derivative information, but to overcome the steepest descent method of slow convergence shortcomings, but also avoid the Newton method needs to be stored and calculate the Hesse matrix and the inverse of the shortcomings of the conjugate gradient method is not only the most useful way to solve the large linear equations, one is also the solution of large-scale nonlinear optimization one of the most effective algorithm. Platform: |
Size: 704512 |
Author: |
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Description: 在无约束最优化方法中，通过选择搜索方向
k
d 而得到的方法主要有四种：最
速下降法，Newton法，共轭方向法，Quasi-Newton (拟牛顿法)。
-Study on the algorithm of several nonlinear conjugate gradient method and the analysis of global convergence Platform: |
Size: 1024 |
Author:李振涛 |
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Description: 非线性规划各种算法汇总，包括线搜索、梯度下降法、牛顿法、共轭梯度法、DFP算法、BFGS算法和信赖域算法-Summary all kinds of algorithm of nonlinear programming, including line search, the gradient descent method, Newton method and conjugate gradient method, DFP algorithm and BFGS algorithm and trust region algorithm Platform: |
Size: 5120 |
Author:夏瀛韬 |
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Description: This paper suggests that a simple modification to the initial search direction can also substantially improve the training efficiency of almost all major optimization methods. It was discovered that if the initial search direction is locally modified by a gain value used in the activation function of the corresponding node, significant improvements in the
convergence rates can be achieved irrespective of the optimization algorithm used. Furthermore the proposed method is robust, easy to compute, and easy to implement into well known nonlinear conjugate gradient algorithms
Platform: |
Size: 180224 |
Author:samir |
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Description: 共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。-Conjugate gradient method (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters. Platform: |
Size: 367616 |
Author:陈怀兵 |
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Search - nonlinear conjugate gradient