function obj= objmixed(Design,rho,kC,kn)
p2=kC(1)+kn; % number of 2-level factors
p3=kC(2)+kC(3); % number of 3-level factors
N=2^p2*3^p3;
p=p2+p3;
n=size(Design,1);

%U is the full model matrix 
%R is the full correlation matrix
%points is all the candidates
U2=[1,-1;1,1];
% the model matrix for qualitative 3-level factor 
U3=[1,-(1.5)^0.5,0.5^0.5;
    1, 0, -2^0.5;
    1, 1.5^0.5, 0.5^0.5];
% the model matrix for quantitative 3-level factor
R2=[1,0; 
    0,(1-rho)/(1+rho)];
% correlation matrix for 3-level qualitative factor
R31=[1, 0, 0;
    0, (1-rho)/(1+2*rho), 0;
    0, 0, (1-rho)/(1+2*rho)];
% correlation matrix for 3-level quantitative factor
tempvalue=3+4*rho+2*rho^4;
R32=[1, 0, -2^0.5*(rho-rho^4)/tempvalue;
     0, 3*(1-rho^4)/tempvalue, 0;
     -2^0.5*(rho-rho^4)/tempvalue, 0, (rho^4-4*rho+3)/tempvalue];
D2=[-1,1]';
D3=[-1,0,1]';
tempU=1;
tempR=1;
points=[-1,1]';

for i=1:p2
    tempU=kron(U2,tempU);
    tempR=kron(R2,tempR);
end
for i=2:p2
    points=[kron([1,1]',points),kron(D2,ones(size(points,1),1))];
end
for i=1:kC(2)
    tempU=kron(U3,tempU);
    tempR=kron(R31,tempR);
    points=[kron([1,1,1]',points),kron(D3,ones(size(points,1),1))];
end
for i=1:kC(3)
    tempU=kron(U3,tempU);
    tempR=kron(R32,tempR);
    points=[kron([1,1,1]',points),kron(D3,ones(size(points,1),1))];
end
U=tempU;
R=tempR;

%flag: indicates the number of control and noise factors in a term
flag=1;
wt=zeros(1,N);
for i=1:kn
    flag=kron([1,2],flag);
end
for i=1:kC(1)
    flag=kron([1,1],flag);
end
for i=1:p3
    flag=kron([1,1,1],flag);
end
wt(flag==2)=1;

D=zeros(n,1);
for l=1:n
for i=1:N
    if sum(Design(l,:)==points(i,:))==p
       D(l)=i;
    end
end
end

UD=U(D,:);

fai=UD*R*UD';
V=R*UD'*inv(fai)*UD*R;
obj=wt*diag(V)/(wt*diag(R));