Learning the Efficient Frontier
Abstract
The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimizations problems with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.
Cite
Text
Chatigny et al. "Learning the Efficient Frontier." Neural Information Processing Systems, 2023.Markdown
[Chatigny et al. "Learning the Efficient Frontier." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/chatigny2023neurips-learning/)BibTeX
@inproceedings{chatigny2023neurips-learning,
title = {{Learning the Efficient Frontier}},
author = {Chatigny, Philippe and Sergienko, Ivan and Ferguson, Ryan and Weir, Jordan and Bergeron, Maxime},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/chatigny2023neurips-learning/}
}