Breaking the Span Assumption Yields Fast Finite-Sum Minimization
Authors: Robert Hannah, Yanli Liu, Daniel O'Connor, Wotao Yin
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we compare the performance of SVRG, and SARAH to SAGA to verify our conclusions. We solve the regularized least squares problem minimize 1/2n ||Ax - b||^2_2 + lambda/2 ||x||^2_2. ... Figure 5.1: Comparison of SAGA, SVRG, and SARAH for various values of the condition number kappa. |
| Researcher Affiliation | Academia | Robert Hannah 1, Yanli Liu 1, Daniel O Connor 2, and Wotao Yin 1 1Department of Mathematics, University of California, Los Angeles 2Department of Mathematics, University of San Francisco |
| Pseudocode | Yes | Algorithm 1 Prox-SVRG(F, x0, eta, m) |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The matrix A and vector b are generated randomly with entries uniformly distributed between 0 and 1. |
| Dataset Splits | No | The paper describes generating random data but does not specify any training, validation, or test dataset splits, or cross-validation methods. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | The parameter lambda is chosen to control the condition number kappa = L/mu of the problem. ... In order to provide a fair comparison, step sizes were tuned individually for each algorithm and each problem instance. |