Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Solving Linear Programs with Fast Online Learning Algorithms
Authors: Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate that our methods can serve as either an approximate direct solver, or an initialization subroutine for exact LP solving. |
| Researcher Affiliation | Academia | 1School of Information Management and Engineering, Shanghai University of Finance and Economics 2Institute for Computational and Mathematical Engineering, Stanford University 3Management Science & Engineering, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Fast online algorithms for LP; Algorithm 2 Online algorithm with duplication |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the described methodology or a link to a repository. |
| Open Datasets | Yes | We use synthetic data from multi-knapsack benchmark. More detailedly, we generate benchmark multi-knapsack problems max Ax b,0 x 1 c, x as discussed in (Chu and Beasley, 1998)... Besides, we collect 13 instances from (Mittelmann, 2022). |
| Dataset Splits | No | The paper describes dataset generation parameters and setup for experiments but does not explicitly provide training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | Yes | We adopt the sifting solver in CPLEX 12.10 as our benchmark solver. ... Table 6. Time of solving MIPLIB instances to an 1e-03 relative accuracy solution and comparison with Gurobi v9.5. |
| Experiment Setup | Yes | Testing Configuration and Setup ... 2). Initial Point. We let online algorithms start from 0. ... 4). Duplication. We allow K {1, 2, 4, 8, 16, 32} 5). Stepsize. We take γ = 1/ sqrt(Kmn). ... 7). Dual Stabilization. We implement a basic dual stabilization procedure which takes α = 0.4 in (8). |