Convex optimization based on global lower second-order models
Authors: Nikita Doikov, Yurii Nesterov
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 first-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 Nikita.Doikov@uclouvain.be Yurii Nesterov Catholic University of Louvain, Louvain-la-Neuve, Belgium Yurii.Nesterov@uclouvain.be |
| 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. |