Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization
Authors: Yue Yu, Longbo Huang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments and compare com SVR-ADMM to existing algorithms. The experimental results are shown in Figure 1 and Figure 2. |
| Researcher Affiliation | Academia | Yue Yu and Longbo Huang Institute for Interdisciplinary Information Sciences, Tsinghua University yu-y14@mails.tsinghua.edu.cn, longbohuang@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 com-SVR-ADMM for strongly convex stochastic composition optimization |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | No | Using the same definition of gj(x) and fi(y) and the same parameters generation method as [Lian et al., 2016], we set the regularization to R(x) = µ 2 ||x||2 2, where µ > 0. The experimental results are shown in Figure 1 and Figure 2. Here the y-axis represents the objective value minus optimal value and the x-axis is the number of oracle calls or CPU time. We set N = 200, n = 2000. In this experiment, the transition probability is randomly generated and then regularized. The reward is also randomly generated. |
| Dataset Splits | No | The paper does not specify exact split percentages or absolute sample counts for training, validation, or test sets, nor does it reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | We set N = 200, n = 2000. cov is the parameter used for reward covariance matrix generation [Lian et al., 2016]. In Figure 1, cov = 2, and cov = 10 in Figure 2. All shared parameters in the four algorithms, e.g., stepsize, have the same values. All shared parameters in four algorithms have the same values. |