Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization
Authors: Bin Gu, Wenhan Xian, Heng Huang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on real high-dimensional gray-scale images not only confirm the fast convergence of our algorithms, but also show a near-linear speedup on a parallel system with shared memory due to the lock-free implementation. |
| Researcher Affiliation | Collaboration | Bin Gu1 , Wenhan Xian2 and Heng Huang2,1 1JD Finance America Corporation 2Department of Electrical & Computer Engineering, University of Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1 Asynchronous Stochastic Frank-Wolfe Algorithm (Asy SFW) and Algorithm 2 Asy SVFW Algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the work described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | The real gray-scale images are available at https://homepages. cae.wisc.edu/ ece533/images/ |
| Dataset Splits | No | The paper describes missing 30% of pixels for the robust matrix completion problem, which is data corruption for the task itself, but does not provide explicit training, validation, and test dataset splits for model evaluation. |
| Hardware Specification | Yes | Our experiments are performed on a 32-core two-socket Intel Xeon E5-2699 machine where each socket has 16 cores. |
| Software Dependencies | No | The paper states implementation using 'C++' and 'Open MP' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Thus, the parameters σ and c are fixed at 0.15 and 500 respectively. In addition, we set the learning rate γ = 0.0001, the mini-batch size b = 500, the inner loop size of Asy SVFW m = 50. We choose X = 1 2 αY as the initial solution for Asy SFW and Asy SVFW, where α is the smallest value in {1, 2, . . . , 10} such that X c. |