最优化——FR共轭梯度法matlab程序

最新推荐文章于 2021-12-11 16:46:54 发布

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最新推荐文章于 2021-12-11 16:46:54 发布 · 9.9k 阅读

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这是一个使用Matlab实现的FR共轭梯度法求解优化问题的程序。程序包括了初始化、搜索方向形成、线性搜索、新点计算等步骤，并在示例函数f1上进行了测试，展示了如何调用该方法进行优化求解。

% cg.m

function [x, output] = cg(fun, dfun, x0)
% fun: character variable, the name of function computing objective function
% dfun: character variable, the name of function computing the gradient of objective function
% x0: real vector, input initial point
% x :real vector, output solution
% output: structure variable, output the value of f, the number ofiteration, the number of function evaluations and the norm of gradient at last point x
%
% Step 1: initialization
%
epsi = 1.0e-3; % termination tolenrence
k = 0; % the number of iteration
funcN = 0; % the number of function evaluation
rho = 0.01; l = 0.15; u = 0.85;
x = x0;
f = feval(fun, x);
funcN = funcN + 1;
n = length(x0);
%
% Step 2: check convergence condition
%
g = feval(dfun, x);
while norm(g) > epsi & k <= 150
%
% Step 3: form search direction
%
iterm = k - floor(k/(n+1))*(n+1);