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..
Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations
Authors: Jisun Park, Ernest K. Ryu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with a numerical experiment on a decentralized semidefinite program (SDP). Figure 1 compares the results of PG-EXTRA and PGEXTRA combined with OHM. Both algorithms normalized iterates and fixed-point residuals converged to v, but OHM is faster for fixed-point residual, as our theory suggests. |
| Researcher Affiliation | Academia | 1Department of Mathematical Sciences, Seoul National University 2Interdisciplinary Program in Artificial Intelligence, Seoul National University. |
| Pseudocode | No | The paper describes algorithms like 'Krasnosel ski ı-Mann iteration (KM)' and 'Halpern iteration (Halpern)' using mathematical equations, but does not present them in a pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the availability of the source code for the methodology described. |
| Open Datasets | No | The experiment uses an 'infeasible semidefinite problem (SDP)' derived from an 'infeasible linear matrix inequality (LMI) designed for this experiment', which does not provide concrete access information or refer to a standard public dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | We used MOSEK (Ap S, 2019) with k = 1, 2, . . . , 100. |
| Experiment Setup | Yes | In this experiment, we use the parameters α = β = 0.01 with n = 10, m = 11, p = 10, and ε = 0.5. |