Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
Authors: Jonathan Lacotte, Yifei Wang, Mert Pilanci
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show in Section 5 the empirical benefits of our adaptive method, compared to several standard optimization baselines. and In this section, we compare adaptive Newton Sketch (NSada) with other optimization methods in regularized logistic regression problems as in Example 4. The datasets used in the numerical experiments are collected from LIBSVM (Chih-Chung & Chih-Jen, 2011). For each dataset, we randomly split it into a training set and a test set with the ratio 1 : 1. |
| Researcher Affiliation | Academia | Jonathan Lacotte 1 Yifei Wang 1 Mert Pilanci 1 1Department of Electrical Engineering, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Effective dimension Newton sketch and Algorithm 2 Adaptive effective dimension Newton sketch |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The datasets used in the numerical experiments are collected from LIBSVM (Chih-Chung & Chih-Jen, 2011). |
| Dataset Splits | Yes | For each dataset, we randomly split it into a training set and a test set with the ratio 1 : 1. |
| Hardware Specification | Yes | All numerical experiments are executed on a Dell Power Edge R840 workstation. Specifically, we use 4 cores with 192GB ram for all compared methods. |
| Software Dependencies | No | The paper mentions software like 'Katyusha' and 'SVRG' and references LIBSVM, but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For SVRG and Katyusha, we use a batch size of 20. |