A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis
Authors: Pinghua Gong, Jieping Ye
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide empirical studies to show that m OWLQN works well and is as efficient as OWL-QN. In this section, we validate the convergence analysis by applying the m OWL-QN algorithm to solve the following ℓ1regularized logistic regression problem: ... Experiments are conducted on four large scale data sets which are summarized in Table 1. ... We report the objective function value vs. CPU time plots in Figure 1. |
| Researcher Affiliation | Academia | Pinghua Gong GONGP@UMICH.EDU University of Michigan, Ann Arbor, MI 48109 Jieping Ye JPYE@UMICH.EDU University of Michigan, Ann Arbor, MI 48109 |
| Pseudocode | Yes | Algorithm 1 OWL-QN: Orthant-Wise Limited memory Quasi-Newton; Algorithm 2 m OWL-QN: modified Orthant-Wise Limited memory Quasi-Newton |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | These data sets are high-dimensional and sparse and can be downloaded from http://www.csie.ntu.edu.tw/ cjlin /libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper uses large-scale datasets but does not specify how these datasets were split into training, validation, or test sets, nor does it mention cross-validation. The focus is on the optimization algorithm itself. |
| Hardware Specification | Yes | Both algorithms are implemented in Matlab and executed on an Intel(R) Core(TM)2 i7-3770 CPU (@3.4GHz) with 32GB memory. |
| Software Dependencies | No | The paper states that algorithms are 'implemented in Matlab' but does not specify a version number for Matlab or any other specific software libraries or dependencies used, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | We terminate both algorithms if the relative change of two consecutive objective function values is less than 10 5 or the number of iterations exceeds 500. We set γ = 10 2, β = 0.2, α0 = 1, ϵ = 10 12 and the number of unrolling steps (in L-BFGS) as m = 10. |