Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
Authors: Wenqing Hu, Chris Junchi Li, Xiangru Lian, Ji Liu, Huizhuo Yuan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on risk-adverse portfolio management validate the superiority of SARAH-Compositional over a few rival algorithms.In this section, we study performance of our algorithm to risk-adverse portfolio management problem and conduct numerical experiments to support our theory. |
| Researcher Affiliation | Collaboration | Wenqing Hu Missouri University of Science and Techology huwen@mst.edu Chris Junchi Li Tencent AI Lab junchi.li.duke@gmail.com Xiangru Lian University of Rochester admin@mail.xrlian.com Ji Liu University of Rochester & Kwai Inc. ji.liu.uwisc@gmail.com Huizhuo Yuan Peking University hzyuan@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 SARAH-Compositional, Online Case (resp. Finite-Sum Case) |
| Open Source Code | Yes | The source code can be found at http://github.com/angeoz/SCGD. |
| Open Datasets | Yes | Datasets include different portfolio datas formed on Size and Operating Profitability. We choose to use 6 different 25-portfolio datasets where N = 25 and T = 7240, same as the ones adopted by Lin et al. (2018). Specifically, we choose SL 1 = SL 2 = SL 3 = 2000 (roughly optimized to improve the numerical performance).The source code can be found at http://github.com/angeoz/SCGD. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html |
| Dataset Splits | No | The paper mentions using 6 different 25-portfolio datasets and specifies mini-batch sizes (SL1, SL2, SL3), but it does not provide explicit details about train/validation/test splits (e.g., percentages, sample counts, or predefined splits with citations) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models, processor types, or memory amounts. It only discusses the experimental setup at a higher level. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages (e.g., Python 3.x) or specific libraries/frameworks (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | Specifically, we choose SL 1 = SL 2 = SL 3 = 2000 (roughly optimized to improve the numerical performance). Our range of stepsize is 1 10 5, 1 10 4, 2 10 4, 5 10 4, 1 10 3, 1 10 2 , and we plot the learning curve for each algorithm corresponding to their individually optimized stepsize. The q-parameters in both SARAH-Compositional and VRSC-PG algorithms are set as 50. |