A Surrogate Objective Framework for Prediction+Programming with Soft Constraints
Authors: Kai Yan, Jie Yan, Chuan Luo, Liting Chen, Qingwei Lin, Dongmei Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method in three applications extended with soft constraints: synthetic linear programming, portfolio optimization, and resource provisioning, demonstrating that our method outperforms traditional two-staged methods and other decision-focused approaches. |
| Researcher Affiliation | Collaboration | Kai Yan Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 kaiyan3@illinois.edu; Jie Yan Microsoft Research Beijing, China jiey@microsoft.com |
| Pseudocode | Yes | The sketch of our algorithm is outlined in Appendix C. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The prediction dataset is daily price data of SP500 from 2004 to 2017 downloaded by Quandl API [24] with the same settings in [3]. |
| Dataset Splits | No | We train each method for 40 epochs, and early stop when valid performance degrades for 4 consecutive epochs. This implies the use of a validation set, but the paper does not specify the explicit size or percentage of this split or how it was generated from the total dataset, apart from mentioning the test set for the ERCOT dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only discusses software and training parameters. |
| Software Dependencies | No | The paper mentions software like PyTorch [17], Gurobi [6], CPLEX [7], Ada Grad [23], and ReLU [22], but does not provide specific version numbers for these software dependencies, making the setup not fully reproducible based on version. |
| Experiment Setup | Yes | All five methods use the same prediction model a fully connected neural network of two hidden layers with 128 neurons for each and Re LU [22] for activation. We use Ada Grad [23] as the optimizer, with learning rate 0.01 and gradient clipped at 1e 4. We train each method for 40 epochs, and early stop when valid performance degrades for 4 consecutive epochs. |