End-to-End Learning for Optimization via Constraint-Enforcing Approximators
Authors: Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian L Quanz, Ioannis Spantidakis
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimental results on two end-to-end problems for a shortest path problem and a capacitated multiproduct newsvendor problem. In this section we present computational results that our Project Net method is effective in end-to-end learning. |
| Researcher Affiliation | Collaboration | Rares Cristian1, Pavithra Harsha2, Georgia Perakis1, Brian L Quanz2, Ioannis Spantidakis1 1 Massachusetts Institute of Technology 2 IBM T. J Watson Research Center |
| Pseudocode | Yes | Algorithm 1 Dykstra s Projection Method; Algorithm 2 Training Project Net; Algorithm 3 End-to-End Learning via Project Net |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about the release of their own source code for the methodology described. |
| Open Datasets | Yes | We use the Warcraft II tile dataset (Guyomarch 2017) which was first introduced in (Vlastelica et al. 2020) to test their end-to-end approach for combinatorial problems. [...] Guyomarch, J. 2017. Warcraft ii open-source map editor. http://github.com/war2/war2edit. |
| Dataset Splits | No | The paper evaluates on 'testing data' but does not provide specific percentages, sample counts, or a clear methodology for dataset splitting into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with their version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We also train a Project Net model with T0 = 5 iterations, and compare the objective value of its solution for iterations up to T1 = 35 on testing data. During training, we only perform T0 iterations of this mapping. In particular, once Project Net has been trained using T0 steps, we may then use some T1 > T0 steps when using it to learn forecasts. Algorithm 2 Training Project Net: Initialize matrix L, Initialize w0 = 0, zj+1 = wj ηL ui wj, wj+1 = πk(zj+1), Update L by any gradient method. Algorithm 3 End-to-End Learning via Project Net: Initialize θ at random. Update θ by any gradient method. |