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数学最优化工具箱,内含0.618法,Fibonacci法,共轭梯度法,你牛顿法,牛顿法,最速下降法。每个算法都有一个实例用matlab实现-Mathematical optimization toolbox, containing 0.618 Fibonacci method, conjugate gradient method, Newton, Newton, steepest descent method. Each algorithm has an example of using mat
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《最优化方法及其Matlab程序设计》较系统地介绍了非线性最优化问题的基本理论和算法,以及主要算法的Matlab程序设计,主要内容包括(精确或非精确)线搜索技术、最速下降法与(修正)牛顿法、共轭梯度法、拟牛顿法、信赖域方法、非线性最小二乘问题的解法、约束优化问题的最优性条件、罚函数法、可行方向法、二次规划问题的解法、序列二次规划法等。-" Optimization Methods and Matlab programming," a more systematic introd
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用vb实现最优化方法中的几种基本算法,包括最速下降法、牛顿法、共轭梯度法、最小二乘法等并结合数据库的使用,程序中还附带用户登录及管理-With vb achieve optimal methods of several basic algorithms, including the steepest descent method, Newton method, conjugate gradient method, least squares method and combined use of
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最速下降法求解最优化问题,梯度法,多项式最有化解-Steepest descent method for solving optimization problems, gradient method, polynomial best to resolve
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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。这里给出共轭梯度法的源程序-Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and
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最速下降法又称为梯度法,是1847 年由著名数学家Cauchy 给出的,它是解析法中最古老的一种,其他解析方法或是它的变形,或是受它的启发而得到的,因此它是最优化方法的基础。这里给出其Matlab源程序。-The steepest descent method, also known as gradient method, was in 1847 given by the famous mathematician Cauchy, it is the oldest analytical metho
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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) is between the steepest descent method and Newton' s
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两种不同编码的黄金分割法 基于信息熵的免疫算法 最速下降法 梯度算法-Two different coding golden section method immune algorithm based on information entropy gradient algorithm steepest descent method
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用matlab分析数值的,有拉个朗日插值、三次样条插值法、NEWTON插值、hermite插值、mewton形式的hermite插值、平方根法、gauss消去法、三角分解法、jacobi迭代法、
gauss迭代法、sor迭代法、最速下降法、共额梯度法、mewton迭代法、逆broyden迭代法。一共15段代码。
-Numerical analysis using matlab, there are a Long Day interpolation, cubic spline interp
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SL0算法是一种基于近似L0范数的压缩感知信号重建算法,它采用最速下降法和梯度投影原理,逐步逼近最优解,具有匹配度高、重建时间短、计算量低、不需要信号的稀疏度这个先验条件等优点。-SL0 algorithm is an approximate L0 norm based compressed sensing signal reconstruction algorithm, which uses the steepest descent method and gradient projection
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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) is between the steepest descent method between a law an
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matlab最速下降法,MATLAB最速下降法(梯度法)程序。-matlab steepest descent method, MATLAB steepest descent method (gradient method) program.
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The architecture of this set of codes and the interaction among them is shown in Figure 1.
The main testing code main code.m calls the code riemann grad unit opt.m that performs
Riemannian optimization under unitary matrix constraint. Steepest De
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预处理的共轭梯度算法和最速下降法MATLAB算法及源程序.-Preconditioning conjugate gradient algorithm and the steepest descent method MATLAB algorithms and source code.
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李津第一次大作业代码:包括共轭梯度法CG,兰索斯Lanczos法、MINRES法、SOR法、最速下降法-Li Jin first big job code: include conjugate gradient method CG, Lanczos Lanczos method, MINRES method, SOR method, the steepest descent method
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共轭梯度法在数值分析中应用非常广泛,比最速下降法要快,这里是FR共轭梯度法,及简单的例子。-Conjugate gradient method in numerical analysis is widely used, and faster than the steepest descent method , this is FR conjugate gradient method, and some simple examples.
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最优化问题中的最速下降法,共轭梯度法,黄金分割法代码,为applied optimization with matlab programming书中源代码-Steepest descent method, conjugate gradient method, golden section method code
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C++实现的最优化算法,包括最速下降法与共轭梯度法-Optimization algorithm implemented in C++, including the steepest descent method and Conjugate Gradient Method
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用MATLAB实现最速下降法_牛顿法和共轭梯度法求解实例-Using MATLAB steepest descent method _ Newton method and conjugate gradient method for solving examples
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最速下降法求解方程f=1/2x *A*x+b *x求其极小值点,其中梯度g=Ax-b,下降方向p=-g,x为初始点,eps为精度。
注:本程序只适用于二次方程,即方程的A矩阵不含未知数。-Steepest descent method for solving the equation f = 1/2x * A* x+ b * x seeking the minimum point where the gradient g = Ax-b, the descent direction p =-
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