Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates
Authors: Quoc Tran Dinh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our algorithms on different numerical examples and compare them with some state-of-the-art methods. |
| Researcher Affiliation | Academia | Quoc Tran-Dinh Department of Statistics and Operations Research, University of North Carolina at Chapel Hill Address: Hanes Hall 333, UNC-Chapel Hill, NC27599, USA. Email: quoctd@email.unc.edu |
| Pseudocode | Yes | Algorithm 1 (Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithm (NEAPAL)) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for Square-root LASSO and pre-processing logo images for low-rank matrix recovery, but does not provide concrete access information (link, DOI, formal citation with author/year) to a publicly available or open dataset used in their experiments. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or test sets) or mention cross-validation setup. |
| Hardware Specification | Yes | All the experiments are implemented in Matlab R2014b, running on a Mac Book Pro. Retina, 2.7GHz Intel Core i5 with 16Gb RAM. |
| Software Dependencies | Yes | All the experiments are implemented in Matlab R2014b, running on a Mac Book Pro. |
| Experiment Setup | Yes | For ASGARD, we use the same setting as in [23], and for Chambolle-Pock s (CP) method, we use step-sizes σ = = k Bk 1 and = 1. In Algorithm 1, we choose 0 := kλ?k k Bkky0 y?k as suggested by Theorem 3.1 to trade-off the objective residual and feasibility gap... In Algorithm 2, we set 0 := µg 4k Bk2 as suggested by our theory, where µg := 0.1 σmin(B) as a guess for the restricted strong convexity parameter. |