Non-Ergodic Convergence Analysis of Heavy-Ball Algorithms
Authors: Tao Sun, Penghang Yin, Dongsheng Li, Chun Huang, Lei Guan, Hao Jiang5033-5040
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results We report the numerical simulations of Heavy-ball method applied to the linear regression problem... As illustrated by Figure 1, larger β leads to faster convergence... |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology, Changsha, Hunan, China. 2Department of Mathematics, University of California, Los Angeles, USA. |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual explanations, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The data Ai and yi were generated by the Gaussian random and Bernoulli random distributions, respectively. |
| Dataset Splits | No | The paper does not provide specific percentages, sample counts, or citations for dataset splits (training, validation, or test) needed for reproduction. |
| Hardware Specification | Yes | All experiments were performed using MATLAB on an desktop with an Intel 3.4 GHz CPU. |
| Software Dependencies | No | The paper mentions 'MATLAB' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | We fixed the stepsize as γ = 1 L in all numerical tests. For the stepsize, we need 2(1 βk) > 1, i.e., 0 βk < 0.5. Therefore, inertial parameters are set to βk β = 0, 0.1, 0.2, 0.3, 0.4. ... And we set n = 100 and m = 150. The data Ai and yi were generated by the Gaussian random and Bernoulli random distributions, respectively. The maximum number of iterations was set to 1000. For logistic regression, we set λ = 10 3. |