单纯型算法和最速下降法的综合算法_能很快的搜索到最小值_matlab

function [x,minf] = minDFP(f,x0,var,eps) format long; if nargin == 3 eps = 1.0e-6; end x0 = transpose(x0); n = length(var); syms l; H = eye(n,n); gradf = jacobian(f,var); v0 = Funval(gradf,var,x0); p = -H*transpose(v0); k = 0; while 1 v = Funval(gradf,var,x0); tol = norm(v); if tol<=eps x = x0; break; end y = x0 + l*p; yf = Funval(f,var,y); [a,b] = minJT(yf,0,0.1); xm = minHJ(yf,a,b); x1 = x0 + xm*p; vk = Funval(gradf,var,x1); tol = norm(vk); if tol<=eps x = x1; break; end if k+1==n x0 = x1; continue; else dx = x1 - x0; dgf = vk - v; dgf = transpose(dgf); dxT = transpose(dx); dgfT = transpose(dgf); mdx = dx*dxT; mdgf = dgf*dgfT; fz = H*(dgf*(dgfT*H)); H = H + mdx/(dxT*dgf)-inv(dgfT*(H*dgf))*fz; p = -H*transpose(vk); k = k+1; x0 = x1; end end minf = Funval(f,var,x); format short;