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 gr

[x,fVal,ExitFlag] = BISECTION(f,LB,UB,target,options) finds x +/- TolX (LB < x < UB) such that f(x) = target +/- TolFun. Any or all of f(scalar), f(array), LB, UB, target, TolX, or TolFun may be scalar or n-di