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..
Geometric Descent Method for Convex Composite Minimization
Authors: Shixiang Chen, Shiqian Ma, Wei Liu
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results on linear regression and logistic regression with elastic net regularization show that Geo PG compares favorably with Nesterov s accelerated proximal gradient method, especially when the problem is ill-conditioned. |
| Researcher Affiliation | Collaboration | Shixiang Chen1, Shiqian Ma2, and Wei Liu3 1Department of SEEM, The Chinese University of Hong Kong, Hong Kong 2Department of Mathematics, UC Davis, USA 3Tencent AI Lab, China |
| Pseudocode | Yes | Algorithm 1 : The first procedure for finding xk from given x+ k 1 and ck 1. Algorithm 2 : The second procedure for finding xk from given x+ k 1 and ck 1. Algorithm 3 : Geo PG: geometric proximal gradient descent for convex composite minimization. Algorithm 4 : Geo PG with Backtracking (Geo PG-B) Algorithm 5 : L-Geo PG: Limited-memory Geo PG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conducted tests on two real datasets downloaded from the LIBSVM repository: a9a, RCV1. We tested Geo PG-B and APG-B for solving (5.2) on the three real datasets a9a, RCV1 and Gisette from LIBSVM |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | The code was written in Matlab and run on a standard PC with 3.20 GHz I5 Intel microprocessor and 16GB of memory. |
| Software Dependencies | No | The paper only mentions 'Matlab' without a specific version number, and no other software dependencies with versions are listed. |
| Experiment Setup | Yes | The initial points were set to zero. The parameters used in backtracking were set to η = 0.5 and γ = 0.9. In the experiments, we ran Algorithm 2 until the absolute value of φ is smaller than 10 8. |