Solving non-linear portfolio optimization problems with the primal-dual interior point method

摘要: Stochastic programming is recognized as a powerful tool to help decision making under uncertainty in financial planning. The deterministic equivalent formulations of these stochastic programs have huge dimensions even for moderate numbers assets, time stages and scenarios per stage. So far models treated by mathematical approaches been limited simple linear or quadratic due the inability currently available solvers solve NLP problems typical sizes. However are highly structured. key efficient solution such therefore ability exploit their structure. Interior point methods well-suited very large non-linear optimization problems. In this paper we feature show how portfolio with sizes measured millions constraints variables, featuring on semi-variance, skewness utility functions objective, can be solved state-of-the-art solver.