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 [1].
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
Authors: Jingwei Liang, Jalal Fadili, Gabriel Peyré
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate our results with some numerical experiments carried out on the problems in Example 1.1, 1.2 and 1.3. The convergence profiles of ||xk x || are shown in Figure 1. |
| Researcher Affiliation | Academia | Jingwei Liang and Jalal M. Fadili Normandie Univ, ENSICAEN, CNRS, GREYC EMAIL Gabriel Peyré CNRS, DMA, ENS Paris EMAIL |
| Pseudocode | Yes | Algorithm 1: A Multi-step Inertial Forward Backward (Mi FB) |
| Open Source Code | No | No explicit statement or link regarding the public release of source code for the described methodology was found. |
| Open Datasets | No | For the problem in Example 1.1, we generated y = Axob + ω with m = 48, n = 128, the entries of A are i.i.d. zero-mean and unit variance Gaussian, xob is 8-sparse, and ω Rm is an additive noise with small variance. For Example 1.3, we generated m = 64 training samples with n = 96-dimensional feature space. This indicates the datasets were generated by the authors for their experiments and no public access information is provided. |
| Dataset Splits | No | The paper describes how data was generated for the experiments (e.g., 'we generated y = Axob + ω', 'we generated m = 64 training samples') but does not specify explicit train/validation/test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') that would be required to replicate the experiments. |
| Experiment Setup | Yes | For all presented numerical results, 3 different settings were tested: the FB method, with γk 0.3/L, noted as FB ; Mi FB with s = 1, bk = ak a and γk 0.3/L, noted as 1-i FB ; Mi FB with s = 2, bi,k = ai,k ai, i = 0, 1 and γk 0.3/L, noted as 2-i FB . We fix γk 0.3/L for all tests. For the 2 inertial schemes, inertial parameters are first chosen such that (2.3) holds. Then between Thm 2.2 and Bnd (2.4) , Bnd (2.4) shows faster convergence result, since the allowed value of P i Iai of (2.4) is bigger than that of Theorem 2.2. |