Most platforms use QP solvers from 1950s Markowitz theory. The theory is sound. The method is not for modern constraints: semi-continuous weights, cardinality limits, issuer rules, turnover caps applied simultaneously. QP solvers handle these poorly or not at all.
Other industries solved this decades ago. Semiconductor fabrication, energy grids, airlines, logistics all use heuristic methods for constraint density that exact solvers cannot reach. Finance is the outlier.
Contrexis is the alternative. A C++ optimizer with reinforcement learning oversight, built for portfolio construction with real institutional constraints. Local execution, no cloud dependency, trade ready output. Data stays in your environment.