Minimize Tracking Error using Quadprog

I have a set of benchmark weights Wb (N X 1) vector and a covariance matrix H (N X N) matrix . I want to get portfolio weights Wp that minimizes tracking error (TE) relative to Wb. So my objectivity function is

= 1/2 * Wp * H * Wp - (H * Wb)' * Wp

Currently the only constraint I have is lb >=0 and sum (Wp) = 1

With these conditions and no constraints, the quadprog should return Wb as the solution set so that with Wp = Wb, TE will be 0

However when I run the function below

nIds = numel(Wb);

Aeq =ones(1,nIds);

beq = 100;

opts = optimset('Algorithm','active-set','MaxIter',10000);

opts = optimset(opts,'tolFun',1e-15);

f = - H * Wb;

[x,fval,exitflag] = quadprog(H,f,[],[],Aeq,beq,lb,[],[],opts);

I get Wp that is close to Wb not exactly Wb. Can someone help?