BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
Authors: Jianming Pan, Zeqi Ye, Xiao Yang, Xu Yang, Weiqing Liu, Lewen Wang, Jiang Bian
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both simulated and real-world datasets demonstrate that BPQP achieves a significant improvement in efficiency typically an order of magnitude faster in overall execution time compared to other differentiable optimization layers. |
| Researcher Affiliation | Collaboration | Jianming Pan1 , Zeqi Ye2 , Xiao Yang3 , Xu Yang3, Weiqing Liu3, Lewen Wang3, Jiang Bian3 1University of California, Berkeley, 2Nankai University 3Microsoft Research Asia |
| Pseudocode | No | The paper describes methods with mathematical equations and figures but does not contain a block specifically labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | We have implemented BPQP with some of the use cases and have released it in the open-source library Qlib[24] (https://github.com/microsoft/qlib). |
| Open Datasets | Yes | We randomly generate three datasets (e.g. simulated constrained optimization) for QPs, LPs, and SOCPs respectively. ... The dataset is from Qlib [24] and consists of 158 sequences, each containing OHLC-based time-series technical features [44] from 2008 to 2020 in daily frequency. Our experiment is conducted on CSI 500 universe which contains at most 500 different stocks each day. |
| Dataset Splits | No | The paper mentions 'early stopping rounds: 5' which implies the use of a validation set, and specifies the time frame for the real-world dataset (2008 to 2020), but it does not provide explicit dataset split percentages or sample counts for training, validation, and test sets. |
| Hardware Specification | Yes | All results were obtained on an unloaded 16-core Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz. qpth runs on an NVIDIA Ge Force GTX TITAN X. |
| Software Dependencies | No | The paper mentions several software packages and libraries used (e.g., CVXPY, qpth/Opt Net, JAXOpt, OSQP) but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We use the same tolerance parameters for simulations experiments: Dual infeasibility tolerance: 1e-04, Primal infeasibility tolerance: 1e-04, Check termination interval: 25, Absolute tolerance: 1e-03, Relative tolerance: 1e-03, ADMM relaxation parameter: 1.6, Maximum number of iterations: 4000. ... MLP predictor: feature size: 153, hidden layer size: 256, number of layersr: 3, dropout rate: 0.Training: number of epoch: 30, learning rate: 1e-4, optimizer: Adam, frequency of rebalancing portfolio: 5 days, risk aversion coefficient: 1, early stopping rounds: 5, the inverse of beta (line 112): 0.1. |