  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.
