Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization
Authors: Zhouyuan Huo, Bin Gu, Ji Liu, Heng Huang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results verify our theoretical analysis. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA 2Department of Computer Science, University of Rochester, Rochester, NY, 14627, USA |
| Pseudocode | Yes | Algorithm 3 VRSC-PG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | No | The paper uses synthetic data generated through Gaussian distributions for portfolio management and generates a Markov decision process (MDP) for reinforcement learning, without providing concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper uses generated synthetic data but does not explicitly specify training, validation, or testing dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiment, we set λ = 10 3 and A = B = b1 = 5. We just select the values of A, B, b1 casually, it is probable that we can get better results as long as we tune them carefully. ... learning rate η is tuned from {1, 10 1, 10 2, 10 3, 10 4}. We keep the learning rate constant for com-SVR-ADMM and VRSC-PG in the optimization. |