我正在尝试在MATLAB中实现“随机梯度下降”。我完全按照算法进行操作，但得到的预测/拟合函数的w（系数）非常非常大。我的算法有错误吗？

算法如下：

x = 0:0.1:2*pi      // X-axis    n = size(x,2);          r = -0.2+(0.4).*rand(n,1);  //generating random noise to be added to the sin(x) function    t=zeros(1,n);    y=zeros(1,n);    for i=1:n        t(i)=sin(x(i))+r(i);          // adding the noise        y(i)=sin(x(i));               // the function without noise    end    f = round(1+rand(20,1)*n);        //generating random indexes    h = x(f);                         //choosing random x points    k = t(f);                         //chossing random y points    m=size(h,2);                     // length of the h vector    scatter(h,k,'Red');              // drawing the training points (with noise)    %scatter(x,t,2);    hold on;    plot(x,sin(x));                 // plotting the Sin function    w = [0.3 1 0.5];                    // starting point of w    a=0.05;                         // learning rate "alpha"// ---------------- ALGORITHM ---------------------//    for i=1:20        v = [1 h(i) h(i).^2];                      // X vector        e = ((w*v') - k(i)).*v;            // prediction - observation        w = w - a*e;                       // updating w    end    hold on;    l = 0:1:6;    g = w(1)+w(2)*l+w(3)*(l.^2);    plot(l,g,'Yellow');                      // drawing the prediction function

回答：

如果使用过大的学习率，SGD很可能会发散。
学习率应该收敛到零。