Derivative-Free Optimization of High-Dimensional Non-Convex Functions by Sequential Random Embeddings
Authors: Hong Qian, Yi-Qi Hu, Yang Yu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply SRE to several state-of-the-art derivative-free optimization methods, and conduct experiments on synthetic functions as well as non-convex classification tasks with up to 100,000 variables. Experiment results verify the effectiveness of SRE. |
| Researcher Affiliation | Academia | Hong Qian, Yi-Qi Hu, and Yang Yu National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {qianh,huyq,yuy}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Sequential Random Embeddings (SRE) |
| Open Source Code | No | The paper mentions implementations of third-party methods are 'by their authors' but does not provide any link or statement for the availability of their own SRE code. |
| Open Datasets | Yes | We employ four binary class UCI datasets [Blake et al., 1998], Gisette, Arcene, Dexter and Dorothea. |
| Dataset Splits | No | The paper uses terms like 'training instance' and mentions testing, but it does not specify explicit percentages or counts for training, validation, and test splits, nor does it provide a splitting methodology or reference predefined splits for reproducibility. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or specific computing environments) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like IMGPO, CMAES, and RACOS but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We set D = 10000, set the total number of function evaluations n = 10000 and the subspace size d = 10, and choose the number of sequential random embeddings m = {1, 2, 5, 8, 10, 20}. ... We set d = 20, the number of function evaluations n = 3D, and X = [ 10, 10]D, Y = [ 10, 10]d, 2 [ 10, 10] for all applied algorithms except for CCCP. ... For algorithms with SRE, we set the number of sequential random embeddings m = 5. |