Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
Authors: Xinyi Hu, Jasper Lee, Jimmy Lee, Allen Z. Zhong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation shows better performance for our proposal over classical and state of the art approaches. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2Department of Computer Sciences & Institute for Foundations of Data Science University of Wisconsin Madison, WI, USA {xyhu,jlee,zwzhong}@cse.cuhk.edu.hk, jasper.lee@wisc.edu |
| Pseudocode | Yes | Algorithm 1: Coordinate Descent. Algorithm 2: Generic Recursive Solving and Recursive Learning. Algorithm 3: Recursive Solving and Recursive Learning for 0-1 Knapsack Problem. |
| Open Source Code | Yes | Our implementation is available at https://github.com/dadahxy/Neur IPS_Branch And Learn |
| Open Datasets | Yes | For MCFP, we use USANet [11] (24 vertices and 43 edges) and GÉANT [10] (40 vertices and 61 edges). For the NP-hard problem of MCVC, we use two smaller graphs from the Survivable Network Design Library [13]: POLSKA (12 vertices and 18 edges) and PDH (11 vertices and 34 edges). |
| Dataset Splits | Yes | We use a 70%/30% training/testing data split. |
| Hardware Specification | Yes | All models are trained with Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz processors. |
| Software Dependencies | No | We use the scikit-learn library [1] to implement LR, k-NN, CART and RF, and OR-Tools [14] as the problem solver in SPO Tree and SPO Forest. The paper does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper describes the general coordinate descent algorithm and some problem-specific parameters (e.g., flow value, number of jobs, feature counts), but it does not specify concrete hyperparameters for the learning process itself (e.g., number of iterations for coordinate descent, specific convergence criteria, or learning rate if applicable). |