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 [1].
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
Authors: Ilya Kuruzov, Gesualdo Scutari, Alexander Gasnikov
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary numerical experiments support our theoretical findings, demonstrating superior performance in convergence speed and scalability. |
| Researcher Affiliation | Academia | Ilya Kuruzov Innopolis University EMAIL. Gesualdo Scutari Purdue University EMAIL. Alexander Gasnikov Innopolis University EMAIL |
| Pseudocode | Yes | Algorithm 1 Data: ... Algorithm 2 Backtracking(...) |
| Open Source Code | Yes | code in the form of an attached archive. |
| Open Datasets | No | Ridge regression: It is an instance of (P), with fi(x) = Aixi bi 2 + Ď xi 2 2, where we set Ai R20 300, bi R20, and Ď = 0.1.The elements of Ai, bi are independently sampled from the standard normal distribution |
| Dataset Splits | No | The paper uses synthetic data generated by sampling elements from a standard normal distribution but does not specify any training, validation, or test splits. |
| Hardware Specification | Yes | All experiments are run on Acer Swift 5 SF514-55TA56B6, with processor Intel(R) Core(TM) i5-8250U @ CPU 1.60GHz, 1800 MHz. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For EXTRA and NIDS we use a grid-search tuning, chosen to achieve the best practical performance. Algorithm 1 and Algorithm 3 are simulated under the following choice of the line-search parameters satisfying Corollary 4.1: γk = (k + 2)/(k + 1), δ = 1. For all the algorithms we used the Metropolis-Hastings weight matrix W GW [34]. |