Learning the Efficient Frontier
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 27 Sep 2023.
Builds on
23 of 47 references.
References · 23
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2020Reformer: The Efficient Transformercited by 1,658
- 2017Deep Setscited by 1,394
- 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
- 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 588
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
11 more
- 2019Differentiable Convex Optimization Layerscited by 458
- 2019Axial Attention in Multidimensional Transformerscited by 350
- 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 341
- 2015Order Matters: Sequence to sequence for setscited by 236
- 2021Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformercited by 108
- 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
- 2020Learning Convex Optimization Modelscited by 16
- 2019GPU Acceleration of ADMM for Large-Scale Quadratic Programmingcited by 15
- 2022Deep Learning the Efficient Frontier of Convex Vector Optimization Problemscited by 2
- 2019Two-Dimensional Batch Linear Programming on the GPUcited by 1
- 2022Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problemscited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.