Abstract: In this talk, we propose a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems satisfying Slater's condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of convex vector optimization problems. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality. Special attention is paid to the mean-variance and mean-risk problems in finance.
Deep learning the efficient frontier of convex vector optimization problems with applications to finance
09.06.2022 15:10 - 16:00
Location:
TU Wien, Freihaus, Gelber Bereich, 10.OG, Seminarraum DB gelb 10, Wiedner Hauptstr. 8, 1040 Wien