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..
Optimal Convergence Rates for Convex Distributed Optimization in Networks
Authors: Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work proposes a theoretical analysis of distributed optimization of convex functions using a network of computing units. We investigate this problem under two communication schemes (centralized and decentralized) and four classical regularity assumptions: Lipschitz continuity, strong convexity, smoothness, and a combination of strong convexity and smoothness. Under the decentralized communication scheme, we provide matching upper and lower bounds of complexity along with algorithms achieving this rate up to logarithmic constants. |
| Researcher Affiliation | Collaboration | Kevin Scaman EMAIL Huawei Noah s Ark Lab, Paris, France Francis Bach EMAIL INRIA, Ecole Normale Sup erieure, PSL Research University, Paris, France S ebastien Bubeck EMAIL Microsoft Research, Redmond, United States Yin Tat Lee EMAIL University of Washington, Seattle, United States Laurent Massouli e EMAIL MSR-INRIA Joint Center, Paris, France |
| Pseudocode | Yes | Algorithm 1 distributed randomized smoothing (convex case) Algorithm 2 distributed randomized smoothing (strongly-convex case) Algorithm 3 multi-step primal-dual algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of source code for the described methodology. |
| Open Datasets | No | The paper is a theoretical analysis of optimization algorithms and does not involve experiments on specific datasets. Therefore, no information about open datasets is provided. |
| Dataset Splits | No | The paper is a theoretical analysis and does not describe any experimental setup involving datasets or their splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and algorithm design for distributed optimization; it does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and presents algorithms without discussing specific software implementations or their versioned dependencies. |
| Experiment Setup | No | The paper focuses on theoretical convergence rates and algorithm design. It does not include an experimental section with specific hyperparameters or system-level training settings. |