文件名称:Opt_Steepest
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用最速下降法求最优化解
输入：f为函数名 grad为梯度函数
x0为解的初值 TolX,TolFun分别为变量和函数的误差阈值
dist0为初始步长 MaxIter为最大迭代次数
输出: xo为取最小值的点 fo为最小的函数值
f0 = f(x(0- Steepest Descent Method with Optimum Solution input: f as a function name grad is gradient function x0 for the solution of the initial TolX, TolFun variables and functions were error threshold dist0 as the initial step MaxIter maximum Diego passage number Output: xo to take the minimum point of fo is the smallest function value f0 = f (x (0))
输入：f为函数名 grad为梯度函数
x0为解的初值 TolX,TolFun分别为变量和函数的误差阈值
dist0为初始步长 MaxIter为最大迭代次数
输出: xo为取最小值的点 fo为最小的函数值
f0 = f(x(0- Steepest Descent Method with Optimum Solution input: f as a function name grad is gradient function x0 for the solution of the initial TolX, TolFun variables and functions were error threshold dist0 as the initial step MaxIter maximum Diego passage number Output: xo to take the minimum point of fo is the smallest function value f0 = f (x (0))
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Opt_Steepest.m