Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations
Authors: Jisun Park, Ernest K. Ryu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |