Yes, mark. The previous one can get the optimal solution, but the following code can’t get the optimal solution.
delta_t=5;
T=120;
N=T/(delta_t);
K=6;
k1=[-1100,600,0];
k2=[-700,-600,0];
k3=[-400,400,0];
k4=[200,200,0];
k5=[600,1200,0];
k6=[800,-400,0];
w=[k1;k2;k3;k4;k5;k6];
R_1=1e+7*ones(N,1);
R_2=2e+7*ones(N,1);
R_3=3e+7*ones(N,1);
R_4=1e+7*ones(N,1);
R_5=2e+7*ones(N,1);
R_6=3e+7*ones(N,1);
R_req=[R_1,R_2,R_3,R_4,R_5,R_6];
H=100;
Bt=5e+6;
beta=0.001;
p=0.1;
delta= 1.0000e-11;
e=2.7182;
E_init=3.996e+5;
Vmax=50;
zeta=0.012;
v0=4.3;
M1=4e+7;
M2=Bt*log(1+(p*beta)/(H^2*delta));
R_tot=0;
for n=1:1:N
qx1(n,1)=-600+1193.5*(cos((2*pi*(n-1)*delta_t)/(T-delta_t)));
qy1(n,1)=233+1193.5*(sin((2*pi*(n-1)*delta_t)/(T-delta_t)));
end
for n=1:1:N
qz(n,:)=H;
end
qz1=qz;
q1=[qx1,qy1,qz1];
for n=1:1:N
for k=1:1:K
gamma(n,k)=(p*beta)/(((norm(q1(n,:)-w(k,:)))^2)*delta);
end
end
for n=1:1:N
for k=1:1:K
RR(n,k)=Bt*log(1+gamma(n,k));
end
end
cvx_begin
cvx_solver mosek
variable a(N,K) binary;
for k=1:1:K
for n=1:1:N
R_tot=R_tot+Bt*a(n,k)*RR(n,k);
end
end
maximize R_tot;
subject to
for n=1:1:N
for k=1:1:K
R_req(n,k)-RR(n,k)<=M1*(1-a(n,k))-1;
RR(n,k)-R_req(n,k)<=M2*a(n,k);
end
end
cvx_end
for n=1:1:N
for k=1:1:K
A(n,k)=-p*beta*delta^2*log(e)/((((norm(q1(n,:)-w(k,:)))^2)*delta^2)*((((norm(q1(n,:)-w(k,:)))^2)*delta^2)+p*beta));
end
end
for n=1:1:N
for k=1:1:K
B(n,k)=Bt*log(1+gamma(n,k));
end
end
cvx_begin
variable Q(N,3)
expression C(N,K)
dual variable yy
for n=1:1:N
for k=1:1:K
C(n,k)=A(n,k)*(pow_pos(norm(Q(n,:)-w(k,:)),2)-pow_pos(norm(q1(n,:)-w(k,:)),2));
end
end
for n=1:1:N
for k=1:1:K
ff(n,k)=a(n,k)*(C(n,k)+B(n,k));
end
end
maximize(sum(sum(ff)))
yy: ff >= R_req;
Q(:,3) >= 50
cvx_end
Status: Infeasible
Optimal value (cvx_optval): -Inf
I think the problem lies in yy: ff>= R_req, How do I deal with this sparse matrix?