Learning the Efficient Frontier

Learning the Efficient Frontier

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...

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Veröffentlicht in:arXiv.org 2023-10
Hauptverfasser: Chatigny, Philippe, Sergienko, Ivan, Ferguson, Ryan, Weir, Jordan, Bergeron, Maxime
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Convexity
Optimization
Resource allocation
Risk levels
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creator Chatigny, Philippe
Sergienko, Ivan
Ferguson, Ryan
Weir, Jordan
Bergeron, Maxime
description 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.
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subjects Convexity
Optimization
Resource allocation
Risk levels
title Learning the Efficient Frontier
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