Robust asymptotic growth in stochastic portfolio theory under long?only constraints

We consider the problem of maximizing asymptotic growth rate an investor under drift uncertainty in setting stochastic portfolio theory (SPT). As work Kardaras and Robertson we take as inputs (i) a Markovian volatility matrix $c(x)$ (ii) invariant density $p(x)$ for market weights, but additionally impose long-only constraints on investor. Our principal contribution is proving uniqueness existence result class concave functionally generated portfolios developing finite dimensional approximation, which can be used to numerically find optimum. In addition general results outlined above, propose use broad models $c(x)$, calibrated data and, which, obtain explicit formulas optimal unconstrained any density.