Adding vs. Averaging in Distributed Primal-Dual Optimization
Authors: Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac
ICML 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a thorough experimental comparison with competing algorithms using several real-world distributed datasets. Our practical results confirm the strong scaling of COCOA+ as the number of machines K grows, while competing methods, including the original COCOA, slow down significantly with larger K. We implement all algorithms in Spark, and our code is publicly available at: github.com/gingsmith/cocoa. |
| Researcher Affiliation | Academia | Chenxin Ma EMAIL Industrial and Systems Engineering, Lehigh University, USA Virginia Smith EMAIL University of California, Berkeley, USA Martin Jaggi EMAIL ETH Z urich, Switzerland Michael I. Jordan EMAIL University of California, Berkeley, USA Peter Richt arik EMAIL School of Mathematics, University of Edinburgh, UK Martin Tak aˇc EMAIL Industrial and Systems Engineering, Lehigh University, USA |
| Pseudocode | Yes | Algorithm 1 COCOA+ Framework |
| Open Source Code | Yes | We implement all algorithms in Spark, and our code is publicly available at: github.com/gingsmith/cocoa. |
| Open Datasets | Yes | The used datasets are summarized in Table 2. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or explicit splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | We implement all algorithms in Apache Spark (Zaharia et al., 2012) and run them on m3.large Amazon EC2 instances |
| Software Dependencies | No | The paper mentions 'Apache Spark' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We compare the COCOA+ and COCOA frameworks directly using two datasets (Covertype and RCV1) across various values of λ, the regularizer, in Figure 1. For each value of λ we consider both methods with different values of H, the number of local iterations performed before communicating to the master. For all runs of COCOA+ we use the safe upper bound of γK for σ . |