牛顿法求解非线性方程组matlab源程序
Newton-Raphson 求解非线性方程组matlab源程序
matlab程序如下:
function hom
[P,iter,err]=newton('f','JF',[7.8e-001;4.9e-001; 3.7e-001],0.01,0.001,1000);
disp(P);
disp(iter);
disp(err);
function Y=f(x,y,z)
Y=[x^2+y^2+z^2-1;
2*x^2+y^2-4*z;
3*x^2-4*y+z^2];
function y=JF(x,y,z)
f1='x^2+y^2+z^2-1';
f2='2*x^2+y^2-4*z';
f3='3*x^2-4*y+z^2';
df1x=diff(sym(f1),'x');
df1y=diff(sym(f1),'y');
df1z=diff(sym(f1),'z');
df2x=diff(sym(f2),'x');
df2y=diff(sym(f2),'y');
df2z=diff(sym(f2),'z');
df3x=diff(sym(f3),'x');
df3y=diff(sym(f3),'y');
df3z=diff(sym(f3),'z');
j=[df1x,df1y,df1z;df2x,df2y,df2z;df3x,df3y,df3z];
y=(j);
function [P,iter,err]=newton(F,JF,P,tolp,tolfp,max)
%输入P为初始猜测值,输出P则为近似解
%JF为相应的Jacobian矩阵
%tolp为P的允许误差
%tolfp为f(P)的允许误差
%max:循环次数
Y=f(F,P(1),P(2),P(3));
for k=1:max
J=f(JF,P(1),P(2),P(3));
Q=P-inv(J)*Y;
Z=f(F,Q(1),Q(2),Q(3));
err=norm(Q-P);
P=Q;
Y=Z;
iter=k;
if (err end end function homework4 [P,iter,err]=newton('f','JF',[7.8e-001;4.9e-001; 3.7e-001],0.01,0.001,1000); disp(P); disp(iter); disp(err); function Y=f(x,y,z) Y=[x^2+y^2+z^2-1; 2*x^2+y^2-4*z; 3*x^2-4*y+z^2]; function y=JF(x,y,z) f1='x^2+y^2+z^2-1'; f2='2*x^2+y^2-4*z'; f3='3*x^2-4*y+z^2'; df1x=diff(sym(f1),'x'); df1y=diff(sym(f1),'y'); df1z=diff(sym(f1),'z'); df2x=diff(sym(f2),'x'); df2y=diff(sym(f2),'y'); df2z=diff(sym(f2),'z'); df3x=diff(sym(f3),'x'); df3y=diff(sym(f3),'y'); df3z=diff(sym(f3),'z'); j=[df1x,df1y,df1z;df2x,df2y,df2z;df3x,df3y,df3z]; y=(j); function [P,iter,err]=newton(F,JF,P,tolp,tolfp,max) %输入P为初始猜测值,输出P则为近似解 %JF为相应的Jacobian矩阵 %tolp为P的允许误差 %tolfp为f(P)的允许误差 %max:循环次数 Y=f(F,P(1),P(2),P(3)); for k=1:max J=f(JF,P(1),P(2),P(3)); Q=P-inv(J)*Y; Z=f(F,Q(1),Q(2),Q(3)); err=norm(Q-P); P=Q; Y=Z; iter=k; if (err end 因篇幅问题不能全部显示,请点此查看更多更全内容