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].
Convex optimization based on global lower second-order models
Authors: Nikita Doikov, Yurii Nesterov
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 7 contains numerical experiments. ... We see, that for bigger D, it becomes harder to solve the optimization problem. Second-order methods demonstrate good performance both in terms of the iterations, and the total computational time. ... In the next set of experiments, we compare the basic stochastic version of our method, using estimators (25) SNewton, the method with the variance reduction (Algorithm 4) SVRNewton, and ο¬rst-order algorithms (with constant step-size, tuned for each problem): SGD and SVRG [21]. |
| Researcher Affiliation | Academia | Nikita Doikov Catholic University of Louvain, Louvain-la-Neuve, Belgium EMAIL Yurii Nesterov Catholic University of Louvain, Louvain-la-Neuve, Belgium EMAIL |
| Pseudocode | Yes | Algorithm 1: Contracting-Domain Newton Method, I ... Algorithm 2: Contracting-Domain Newton Method, II ... Algorithm 3: Aggregating Newton Method ... Algorithm 4: Stochastic Variance-Reduced Contracting-Domain Newton |
| Open Source Code | Yes | The source code can be found at https://github.com/doikov/contracting-newton/ |
| Open Datasets | Yes | The composite part is given by (4), with p = 2. ... determined by the dataset4. 4https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, percentages, or absolute sample counts needed to reproduce the experiment. |
| Hardware Specification | Yes | Clock time was evaluated using the machine with Intel Core i5 CPU, 1.6GHz; 8 GB RAM. |
| Software Dependencies | No | The paper states 'All methods were implemented in C++', but does not provide specific version numbers for compilers, libraries, or other software dependencies. |
| Experiment Setup | No | The paper mentions 'constant step-size, tuned for each problem' for some algorithms, but it does not provide specific hyperparameter values like learning rates, batch sizes, number of epochs, or other detailed training configurations for reproducibility. |