Optimal value (cvx_optval): +Inf

I met some problem in optimization problem
my code is

while(error1 > epsilon && error2 > epsilon && r1 < r_max)
    e_old  = ones(M,1);
    lambda = 10;   lambda_max = 1000;
    rho_old = [1,1,1];
    a_old = [1;1;1];
    for k = 1:K
        mu_1(k) = 1/(rho_old(k) +1);
        m11(k) = 1/(1+a_old(k)*p(k));
    end  
    cvx_begin sdp
    cvx_solver mosek
    variables e(M,1)  b(M,1)  c(M,1)  mu_e(1,K) mu_g(1,K) kappa(1,K) ...
        a(1,K) rho(1,K) d(1,K)
    expression ex(1,K)
    obj =sum(kappa) + lambda*(sum(b) + sum(c)); %
    maximize obj
    subject to
    for k = 1:K
        [a(k) - M*mu_g(k) - mu_e(k)       (H_UE(:,k) + H_IE*diag(H_UI(:,k))*e)'; ...
            (H_UE(:,k) + H_IE*diag(H_UI(:,k))*e)                   ones(E,E)  ]>=0;
        mu_g(k)>=0;
        mu_e(k)>=0;
        log(rho(k) + d(k) +1) - mu_1(k)*(rho(k)+1) + log(mu_1(k)) + 2  - m11(k)*(a(k)*p(k)+1) + log(m11(k)) >= r(k) + kappa(k);
        p(k)*(2*real((H_UB(:,k)'+e_old'*H(:,:,k)')*W(:,k)*W(:,k)'*(H_UB(:,k)+H(:,:,k)*e))) - abs((H_UB(:,k)'+e_old'*H(:,:,k)')*W(:,k))^2 >= d(k);
        for j = k+1:K
          ex(j) = p(j)*abs((H_UB(:,j)'+e_old'*H(:,:,j)')*W(:,k))^2;
        % ex(j) = p(j)*(2*real((H_UB(:,j)'+e_old'*H(:,:,j)')*W(:,k)*W(:,k)'*(H_UB(:,j)+H(:,:,j)*e))) - abs((H_UB(:,j)'+e_old'*H(:,:,j)')*W(:,k))^2;
        end
        sum(ex)<= rho(k);
        for m = 1:M
            2*real(e_old(m)*e(m))- abs(e_old(m))^2 >= 1 - b(m);
            e(m)*conj(e(m)) <= 1 + c(m)
        end
    end
    cvx_end
    error1 = sum(c) + sum(b);
    error2 = norm(e-e_old,1);  %向量的一范数,各元素绝对值之和
    lambda = min(t*lambda,lambda_max);
    r1=r1+1;
end

but the result is
Problem
Name :
Objective sense : min
Type : CONIC (conic optimization problem)
Constraints : 48
Cones : 18
Scalar variables : 96
Matrix variables : 3
Integer variables : 0

Optimizer started.
Presolve started.
Eliminator - tries : 0 time : 0.00
Lin. dep. - tries : 0 time : 0.00
Lin. dep. - number : 0
Presolve terminated. Time: 0.00
Optimizer terminated. Time: 0.05

Interior-point solution summary
Problem status : PRIMAL_INFEASIBLE
Solution status : PRIMAL_INFEASIBLE_CER
Dual. obj: 1.0000000000e+01 nrm: 1e+00 Viol. con: 0e+00 var: 0e+00 barvar: 0e+00 cones: 0e+00
Optimizer summary
Optimizer - time: 0.05
Interior-point - iterations : 0 time: 0.02
Basis identification - time: 0.00
Primal - iterations : 0 time: 0.00
Dual - iterations : 0 time: 0.00
Clean primal - iterations : 0 time: 0.00
Clean dual - iterations : 0 time: 0.00
Simplex - time: 0.00
Primal simplex - iterations : 0 time: 0.00
Dual simplex - iterations : 0 time: 0.00
Mixed integer - relaxations: 0 time: 0.00


Status: Unbounded
Optimal value (cvx_optval): +Inf

As we all know, the value of H and W in the above code are about 1. Why the problem is unbounded.

As we all know, the value of H and W in the above code are about 1.

How do we know that? (There is not even any H in your program.) And even if we did know that, how does that show the problem is not unbounded?

You haven’t provided all the input data, so none of the forum readers can even run the first CVX invocation.

Follow the advice at https://yalmip.github.io/debuggingunbounded