A Fast Optimal Control Algorithm for Multi-Period Portfolio Optimization

This paper contributes towards the development of a fast algorithm, relying on the Alternating-Direction of Multipliers (ADMM), for solving scenario-based Model Predictive Control arising in multi-period portfolio... [ view full abstract ]

This paper contributes towards the development of a fast algorithm, relying on the Alternating-Direction of Multipliers (ADMM), for solving scenario-based Model Predictive Control arising in multi-period portfolio optimization problems �efficiently.

We enhance the standard two-set splitting algorithm of the ADMM method, by including inequality constraints through a so-called embedded splitting, without recourse to an additional splitting set. We derive an alteration of the termination criterion, using the probabilities assigned to the scenarios and provide a convergence analysis. We show that the proposed criterion outperforms the standard approach and highlight our results with a numerical comparison with a state-of-the-art algorithm.