I have a portfolio optimization problem where my objective function is the mean divided by the standard deviation.
The variance is the difference of two random variables so is computed as Var(X) + Var(Y) - 2 * Cov(X, Y). The variance term is specified as above, where w represents the portfolio selection, capital sigma is a covariance matrix, and sigma sub delta g is a vector of covariances related to the second random variable. The problem is that CVXPY doesn't consider the last term there to be nonnegative because some of the covariance terms are negative. Obviously, I know that the variance will always be nonnegative, so I believe that this should work as a quasiconvex problem. Is there any way to tell CVXPY that this variance term will always be positive?
