>> c=[-5,-4,-6];

>> A=[1 -1 1

3 2 4

3 2 0];

>> b=[20;42;30];

>> lb=zeros(3,1);

>> [x,fval,exitflag,output,lambda]=linprog(c,A,b,[],[],lb)

Optimization terminated.

x =

    0.0000

   15.0000

    3.0000

fval =

  -78.0000

exitflag =

     1

output = 

         iterations: 6

          algorithm: 'interior-point-legacy'

       cgiterations: 0

            message: 'Optimization terminated.'

    constrviolation: 0

      firstorderopt: 5.8705e-10

lambda = 

    ineqlin: [3x1 double]

      eqlin: [0x1 double]

      upper: [3x1 double]

      lower: [3x1 double]

>>

function[c,ceq]=mycon(x)

c1=1.5+x(1)*x(2)-x(1)-x(2);

c2=-x(1)*x(2)-10;

c=[c1;c2];       % 非线性不等式约束

ceq = [];        % 非线性等式约束

>> optf=@(x) exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1);

>> x0=[10;10];lb=[0;0];ub=[inf;inf];

>> options=optimset('Algorithm','active-set','Display','off');

>> [x,fval] = fmincon(optf,x0,[],[],[],[],lb,ub,@mycon,options)

x =

         0

    1.5000

fval =

    8.5000

>>

>> x=0:.1:5;

>> y=sin(x);

>> f=@(b,x)  b(1)*cos(b(2)*x+b(3))+b(4);

>> [a,ss]=lsqcurvefit(f,[1,1,1,1],x,y)

Local minimum found.

Optimization completed because the size of the gradient is less than

the default value of the function tolerance.

<stopping criteria details>

a =

   -1.0000    1.0000    1.5708   -0.0000

ss =

   8.7594e-29

>> xf=0:0.05:8;

yfit=f(a,xf);

plot(xf,sin(xf),'ro',xf,yfit,'b-')

>> zf=@(a,x) a(1)*x(:,1).^2+a(2)*x(:,2).^2+a(3) %构造拟合函数类型

zf = 

    @(a,x)a(1)*x(:,1).^2+a(2)*x(:,2).^2+a(3)

>> [X0,Y0]=meshgrid(-2:.2:2);

x0=X0(:);y0=Y0(:);

z0=zf([1,1,-1],[x0,y0]);

>> %用上面的点去拟合估计曲面函数中的参数a:

>> [b,re]=nlinfit([x0,y0],z0,zf,[0,0,0]);

b %显示b，发现恰好是1、1、-1

b =

    1.0000    1.0000   -1.0000

>> [X,Y] = meshgrid(-3:.25:3);Z = peaks(X,Y);

[XI,YI] = meshgrid(-3:.125:3);

ZI = interp2(X,Y,Z,XI,YI);

mesh(X,Y,Z), hold, mesh(XI,YI,ZI+15), hold off

axis([-3 3 -3 3 -5 20])

已锁定最新绘图

>>