Inverse Optimization via Learning Feasible Regions
Authors: Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Synthetic experiments and two power system applications, including comparisons with state-of-the-art approaches, showcase and validate the proposed approach. |
| Researcher Affiliation | Collaboration | 1Amazon 2University of Toronto and Delft University of Technology 3University of Cyprus. Correspondence to: Ke Ren <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Gradient descent based algorithm for (10) using predictability loss |
| Open Source Code | No | No explicit statement or link indicating the release of source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We apply our methods on an instance of a power system described in (Bampou & Kuhn, 2011). ... We further investigate the performance of the proposed approach using the larger IEEE 14-bus system (Leon et al., 2020). |
| Dataset Splits | Yes | We generate Ntrain = 100 data points for training and Ntest = 200 data points for testing, by generating signals uniformly at random from scost 1 [0.2, 1], scost 2 [0.2, 0.5], scost 3 [1, 2] and sdemand 1 [0.3, 1.5], sdemand 2 [0.36, 1.8], sdemand 3 [0.42, 2.1], sdemand 4 [0.48, 2.4] and sdemand 5 [0.54, 2.7], and solving problem (14) to obtain pairs {si, xi}N=100 i=1 . |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. It mentions Gurobi, which is a solver, not hardware. |
| Software Dependencies | Yes | We use the hypothesis (7) where Z is the unit simplex of dimension p {3, 6, 9} and solve problems (10) using the non-convex quadratic solver of Gurobi v11.0.3 with a time limit of 1800 seconds. |
| Experiment Setup | Yes | We use the hypothesis (7) where Z is the unit simplex of dimension p {3, 6, 9} using the Adaptive Smoothing Algorithm 2 with a limit of 3000 iterations. ... The initial values are ϵ1 = ϵ2 = 1, and every time the change in the values of PN i=1 γs1 2 and PN i=1 γs2 2 are less than 0.01/10(log2(ϵ1)+1), we multiply the parameters ϵ1 and ϵ2 by 2. |