Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
Authors: Xiaotong Yuan, Ping Li, Tong Zhang
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods when applied to learning tasks of sparse logistic regression and sparse support vector machines. |
| Researcher Affiliation | Academia | Xiao-Tong Yuan XTYUAN1980@GMAIL.COM Department of Statistical Science, Cornell University, Ithaca, NY 14853, USA Dept. of Statistics & Biostatistics, Dept. of Computer Science, Rutgers University, Piscataway, NJ 08854, USA Ping Li PINGLI@STAT.RUTGERS.EDU Dept. of Statistics & Biostatistics, Dept. of Computer Science, Rutgers University, Piscataway, NJ 08854, USA Tong Zhang TZHANG@STAT.RUTGERS.EDU Dept. of Statistics & Biostatistics, Rutgers University, Piscataway, NJ 08854, USA |
| Pseudocode | Yes | Algorithm 1: Gradient Hard Thresholding Pursuit (Gra HTP). Initialization: x(0) with x(0) 0 k (typically x(0) = 0), t = 1. Output: x(t). repeat (S1) Compute x(t) = x(t 1) η f(x(t 1)); (S2) Let F (t) = supp( x(t), k) be the indices of x(t) with the largest k absolute values; (S3) Compute x(t) = arg min{f(x), supp(x) F (t)}; t = t + 1; until halting condition holds; Fast Gra HTP repeat Compute x(t) = x(t 1) η f(x(t 1)); Compute x(t) = x(t) k as the truncation of x(t) with top k (in magnitude) entries preserved; t = t + 1; until halting condition holds; |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper mentions specific datasets 'rcv1.binary' and 'news20.binary' but does not provide a direct URL, DOI, specific repository name, or a formal citation with author names and year for public access to these datasets. |
| Dataset Splits | Yes | For rcv1.binary, a training subset of size 20,242 and a testing subset of size 20,000 are used. For news20.binary, a training subset of size 10,000 and a testing subset of size 9,996 are used. |
| Hardware Specification | Yes | All the considered algorithms are implemented in Matlab 7.12 running on a desktop with Intel Core i7 3.2G CPU and 16G RAM. |
| Software Dependencies | Yes | All the considered algorithms are implemented in Matlab 7.12 running on a desktop with Intel Core i7 3.2G CPU and 16G RAM. |
| Experiment Setup | Yes | We fix the regularization parameter λ = 10 4 in the objective of (6). ... We simply initialize w(0) = 0 and set the stopping criterion as w(t) w(t 1) / w(t 1) 10 4. |