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..
First-Order Methods for Large-Scale Market Equilibrium Computation
Authors: Yuan Gao, Christian Kroer
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that Proportional Response dynamics is highly efficient for computing approximate market equilibria, while projected gradient with linesearch can be much faster when higher-accuracy solutions are needed. We perform numerical experiments on market instances under all three utilities with various generated parameters. |
| Researcher Affiliation | Academia | Yuan Gao Department of IEOR, Columbia University New York, NY, 10027 EMAIL Christian Kroer Department of IEOR, Columbia University New York, NY, 10027 EMAIL |
| Pseudocode | Yes | We prove that proximal gradient with a non-standard practical linesearch scheme (Algorithm 1 in the appendix) converges linearly |
| Open Source Code | Yes | Codes for the numerical experiments are available at https://github.com/Coffee And Convexity/fom-for-me-codes. |
| Open Datasets | No | For linear utilities, we generate market data v = (vij) where vij are i.i.d. from standard Gaussian, uniform, exponential, or lognormal distribution. For each of the sizes n = 50, 100, 150, 200 (on the horizontal axis) and m = 2n, we generate 30 instances with unit budgets Bi = 1 and random budgets Bi = 0.5 + Bi (where Bi follows the same distribution as vij). |
| Dataset Splits | No | The paper describes generating synthetic data and uses a termination criterion based on convergence error, not standard training/validation/test dataset splits. |
| Hardware Specification | No | Part of the numerical experiments were run on the computing server of Columbia University Data Science Institute https://datascience.columbia.edu/about-us/work-with-us/computing-resources/. This only states the location, not specific hardware components like CPU/GPU models. |
| Software Dependencies | No | The paper mentions CVXPY and Mosek but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For PGLS, we report the number of linesearch iterations (that is, the total number of projection computations). For other algorithms, we report the number of iterations. As a fair comparison, we use the same parameters α, β, Γ for PGLS throughout without handpicking. |