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Computer Science > Machine Learning

arXiv:2309.15775 (cs)
[Submitted on 27 Sep 2023 (v1), last revised 13 Oct 2023 (this version, v2)]

Title:Learning the Efficient Frontier

Authors:Philippe Chatigny, Ivan Sergienko, Ryan Ferguson, Jordan Weir, Maxime Bergeron
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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 optimization problem 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.
Comments: Accepted at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
Subjects: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2309.15775 [cs.LG]
  (or arXiv:2309.15775v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2309.15775

Submission history

From: Philippe Chatigny [view email]
[v1] Wed, 27 Sep 2023 16:49:37 UTC (1,982 KB)
[v2] Fri, 13 Oct 2023 19:03:03 UTC (2,008 KB)
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