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..
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Authors: Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that our method achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and block-separability of the proximal term. Empirical benchmarks on a multi-core architecture illustrate practical speedups of up to 12x on a 20-core machine. |
| Researcher Affiliation | Academia | Fabian Pedregosa INRIA/ENS Paris, France Remi Leblond INRIA/ENS Paris, France Simon Lacoste-Julien MILA and DIRO Universit e de Montr eal, Canada DI Ecole normale sup erieure, CNRS, PSL Research University |
| Pseudocode | Yes | Algorithm 1 PROXASAGA (analyzed) ... Algorithm 2 PROXASAGA (implemented) |
| Open Source Code | Yes | A reference C++/Python implementation of is available at https://github.com/fabianp/ProxASAGA |
| Open Datasets | Yes | Table 1: Description of datasets. Dataset n p density L Δ KDD 2010 (Yu et al., 2010) 19,264,097 1,163,024 10 6 28.12 0.15 KDD 2012 (Juan et al., 2016) 149,639,105 54,686,452 2 10 7 1.25 0.85 Criteo (Juan et al., 2016) 45,840,617 1,000,000 4 10 5 1.25 0.89 |
| Dataset Splits | No | The paper uses large-scale datasets and evaluates convergence and speedup, but does not explicitly describe specific train/validation/test dataset splits, ratios, or cross-validation methodology. |
| Hardware Specification | No | The paper mentions 'on a multi-core architecture' and 'on a 20-core machine' but does not provide specific details such as CPU/GPU models, memory, or other hardware specifications used for the experiments. |
| Software Dependencies | No | The paper mentions a 'C++/Python implementation' but does not provide specific version numbers for any software dependencies, libraries, or compilers used in the experiments. |
| Experiment Setup | Yes | The objective function takes the form i=1 log 1 + exp( bia i x) + λ1 2 x 2 2 + λ2 x 1 , where ai Rp and bi { 1, +1} are the data samples. Following Defazio et al. (2014), we set λ1 = 1/n. The amount of 1 regularization (λ2) is selected to give an approximate 1/10 nonzero coefficients. ... We use the following step size: 1/2L for PROXASAGA, 1/Lc for ASYSPCD, where Lc is the coordinate-wise Lipschitz constant of the gradient, while FISTA uses backtracking line-search. |