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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Authors: Yanli Liu, Fei Feng, Wotao Yin
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our numerical experiments, we observe an on average 8 speedup on the number of iterations and 7 speedup on runtime. 5. Experiments To investigate the practical performance of Algorithms 1 and 2, we test on three problems: Lasso, logistic regression, and a synthetic sum-of-nonconvex problem. In the following, we compare SVRG, i Pre SVRG, Katyusha X, and i Pre Kat X on four datasets from LIBSVM1: w1a.t (47272 samples, 300 features), protein (17766 samples, 357 features), cod-rna.t (271617 samples, 8 features), australian (690 samples, 14 features), and one synthetic dataset. Our numerical results are presented in the following figures. |
| Researcher Affiliation | Academia | Yanli Liu 1 Fei Feng 1 Wotao Yin 1 1Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, USA. Correspondence to: Yanli Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Inexact Preconditioned SVRG(i Pre SVRG) Algorithm 2 Inexact Preconditioned Katyusha X(i Pre Kat X) Procedure 1 Procedure for solving (3.2) inexactly Algorithm 3 FISTA with restart for solving (3.2) |
| Open Source Code | Yes | The code is available at: https://github.com/uclaopt/IPSVRG. |
| Open Datasets | Yes | We compare SVRG, i Pre SVRG, Katyusha X, and i Pre Kat X on four datasets from LIBSVM1: w1a.t (47272 samples, 300 features), protein (17766 samples, 357 features), cod-rna.t (271617 samples, 8 features), australian (690 samples, 14 features), and one synthetic dataset. 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper discusses epoch length and general experimental settings but does not explicitly mention train/validation/test splits, specific sample counts for splits, or cross-validation methodology. |
| Hardware Specification | Yes | The experiments are conducted on a Windows system with Intel Core i7 2.6 GHz CPU. |
| Software Dependencies | Yes | All algorithms are implemented in Matlab R2015b. |
| Experiment Setup | Yes | 1. We choose the epoch length m = 100 in all experiments, since we found that the choices m { n 2 , n} need more gradient evaluations. 2. For i Pre PDHG and i Pre Kat X, we use FISTA as the subproblem iterator S. If the preconditioner M is diagonal, then the number of subroutines for solving the subproblem is p = 1, if not, then we set p = 20. 3. In all the experiments, we tune the step size η and momentum weight τ to their optimal. 4. All algorithms are initialized at x0 = 0. |