Description: Conjugate Gradient Minimization在梯度下降算法中有着重要应用。可以解决一些一般方法不容易解决的问题-Conjugate Gradient Minimization in the gradient descent algorithm has important applications. General approach can solve some difficult problems Platform: |
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Author:ZhangGeng |
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Description: 一个介绍最优化方法课件，很详细。从最简单的一维搜索到共轭梯度，最速下降法等等，都有说明-An optimization method introduction courseware, in great detail. From the simplest one-dimensional search to the conjugate gradient, steepest descent method, etc., all indicate Platform: |
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Author:lgl |
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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: |
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Author:孙翔 |
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Description: 压缩包里包含了无约束优化问题常用的几种求解方法的源程序：变量轮换法（variable_rotation.m）、最速下降法（steepest_descent.m）、修正牛顿法（modified_newton.m）、共轭梯度法（conjugate_gradient.m）。另外，coefficient_matrix.m为目标函数系数获得矩阵，minval.m为最小值计算函数，gradient.m为梯度计算函数-Compression bag contains unconstrained optimization problems of several commonly used method of source: variable rotation Act (variable_rotation.m), steepest descent method (steepest_descent.m), as amended Newton (modified_newton.m), conjugate gradient method (conjugate_gradient.m). In addition, coefficient_matrix.m as the objective function coefficients obtained matrix, minval.m function for the minimum calculation, gradient.m for gradient calculation function Platform: |
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Author:闫安心 |
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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: |
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Author:ljw |
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Description: 1． 程序运行时首先输入要求解第几题，然后输入初始点，即可得到最后结果。从程序运行结果中我们可以看到，采用共轭梯度法所得的结果比采用最速下降法所得的结果更为精确，这是由于：共轭梯度法实质上是对最速下降法的修正，使搜索方向变为共轭方向，即每一步的搜索方向都要对该步的负梯度进行修正。-1. Program run-time solution of the first type the first few questions asked, and then enter the initial point, final results can be obtained. The results from the program is running, we can see that the conjugate gradient method using the results of the steepest descent method than using the results of more precise, this is due to: the conjugate gradient method in essence is the steepest descent method' s amendment to make the search conjugate direction into the direction that every step of the search direction must be the negative gradient of the steps to be amended. Platform: |
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Author:陈卫亮 |
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Description: LM算法
老外写的The Levenberg-Marquardt (LM) algorithm is the most widely used optimization algorithm. It
outperforms simple gradient descent and other conjugate gradient methods in a wide variety of
problems. This document aims to provide an intuitive explanation for this algorithm. The LM
algorithm is fi rst shown to be a blend of vanilla gradient descent and Gauss-Newton iteration.
Subsequently, another perspective on the algorithm is provided by considering it as a trust-region
method-The Levenberg-Marquardt (LM) algorithm is the most widely used optimization algorithm. It
outperforms simple gradient descent and other conjugate gradient methods in a wide variety of
problems. This document aims to provide an intuitive explanation for this algorithm. The LM
algorithm is fi rst shown to be a blend of vanilla gradient descent and Gauss-Newton iteration.
Subsequently, another perspective on the algorithm is provided by considering it as a trust-region
method Platform: |
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Author:TANG |
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Description: matlab最优化程序包括 无约束一维极值问题 进退法 黄金分割法 斐波那契法 牛顿法基本牛顿法 全局牛顿法 割线法 抛物线法 三次插值法 可接受搜索法 Goidstein法 Wolfe.Powell法 单纯形搜索法 Powell法 最速下降法 共轭梯度法 牛顿法 修正牛顿法 拟牛顿法 信赖域法 显式最速下降法-matlab optimization program includes one-dimensional extremum problem without constraint advance and retreat method Fafei Bo Fibonacci golden section method the basic Newton method, global Newton method Newton secant parabola method acceptable to the three interpolation methods Wolfe.Powell search method Goidstein simplex method Searching Powell steepest descent method conjugate gradient method modified Newton method Newton Newton trust region method to be explicitly steepest descent method Platform: |
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Author:林小博 |
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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: |
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Author:sunling |
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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: |
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Author: |
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Description: 压缩包里包含了无约束优化问题常用的几种求解方法的源程序：变量轮换法（variable_rotation.m）、最速下降法（steepest_descent.m）、修正牛顿法（modified_newton.m）、共轭梯度法（conjugate_gradient.m）。另外，coefficient_matrix.m为目标函数系数获得矩阵，minval.m为最小值计算函数，gradient.m为梯度计算函数-Compression bag contains unconstrained optimization problems of several commonly used method of source: variable rotation Act (variable_rotation.m), steepest descent method (steepest_descent.m), as amended Newton (modified_newton.m), conjugate gradient method (conjugate_gradient.m). In addition, coefficient_matrix.m as the objective function coefficients obtained matrix, minval.m function for the minimum calculation, gradient.m for gradient calculation function-Compression bag contains unconstrained optimization problems of several commonly used method of source: variable rotation Act (variable_rotation.m), steepest descent method (steepest_descent.m), as amended Newton (modified_newton.m), conjugate gradient method (conjugate_gradient.m). In addition, coefficient_matrix.m as the objective function coefficients obtained matrix, minval.m function for the minimum calculation, gradient.m for gradient calculation function Platform: |
Size: 1024 |
Author:zhuyuanli |
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Description: 只是一个关于用共轭梯度下降算法处理数据的程序，本程序处理的数据对象为MINST,该程序在线性搜索上有很好的效果-Only one on the use of conjugate gradient descent algorithm data processing procedures, the procedures for processing the data object is MINST, linear search for the program have a good effect on Platform: |
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Author:gemini |
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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 - conjugate gradient descent