How to solve the issue "value of type 'cvx' is not convertible to 'double'."

Kindly suggest me in following code, as I am getting a error.

Unable to perform assignment because value of type ‘cvx’ is not convertible to ‘double’.

The following is code,

M=8; K=3; N0=10^-12;
for i=1:K
variable f(K) nonnegative;
variable a_k(M,K); >>>>>>>Doubt: is this correct assignment? a_k is a matrix of M*K
% Objective function
R_k(i)=-rel_entr(N0,N0+f(i)*v1(i))/log(2); % v1(i) is already predetermined value.
maximize : I want to maximize sum of R_k(i) i.e R_k(1)+ R_k(2)+ R_k(3)
% Constraints
subject to

With that code, as discussed in The Basics — CVX Users' Guide , you need to declare
expression R_k(K)
before first using R_k.

However, rel_entr can accept vector arguments. So you need not ever explicitly form R_k or use or declare it. You can just sum(rel_entr(....).

help rel_entr

rel_entr Scalar relative entropy.
rel_entr(X,Y) returns an array of the same size as X+Y with the
relative entropy function applied to each element:
{ X.*LOG(X./Y) if X > 0 & Y > 0,
rel_entr(X,Y) = { 0 if X == 0 & Y >= 0,
{ +Inf otherwise.
X and Y must either be the same size, or one must be a scalar. If X and
Y are vectors, then SUM(rel_entr(X,Y)) returns their relative entropy.
If they are PDFs (that is, if X>=0, Y>=0, SUM(X)==1, SUM(Y)==1) then
this is equal to their Kullback-Liebler divergence SUM(KL_DIV(X,Y)).
-SUM(rel_entr(X,1)) returns the entropy of X.

Disciplined convex programming information:
    rel_entr(X,Y) is convex in both X and Y, nonmonotonic in X, and
    nonincreasing in Y. Thus when used in CVX expressions, X must be
    real and affine and Y must be concave. The use of rel_entr(X,Y) in
    an objective or constraint will effectively constrain both X and Y 
    to be nonnegative, hence there is no need to add additional
    constraints X >= 0 or Y >= 0 to enforce this.
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