I am trying to find a set of weights for each asset in the portfolio, that would give me the highest geometric mean, with only the constraint that all weights must sum up to 1. I'm currently using the SciPy minimize function with a Sequential Least SQuares Programming (SLSQP) method.
However, I'm not sure if this is the best way for achieving the maximum geometric mean, since it's not a quadratic function. What would be the best algorithm for this situation?