A Composite Randomized Incremental Gradient Method
Authors: Junyu Zhang, Lin Xiao
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments in Section 5. In this part, we present numerical experiments for two applications: risk-averse portfolio optimization and policy evaluation for Markov decision processes. Figure 1. Experiments on the risk-averse portfolio optimization problem. Figure 2. Experiments on MDP policy evaluation problem. |
| Researcher Affiliation | Collaboration | 1Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, USA. 2Microsoft Research, Redmond, Washington, USA. |
| Pseudocode | Yes | Algorithm 1 Composite SAGA (C-SAGA) |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | In our experiments, the reward vectors Ri are first generated as n i.i.d Gaussian random vectors with a random correlation matrix C = LLT, where L Rd d satisfies N(0, 1) distribution elementwise. In the experiments, Pπ, Φ and Rπ are generated randomly. |
| Dataset Splits | No | The paper uses randomly generated data for its experiments and does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Both VRSC-PG and C-SAGA use the same step size η = 0.001 and batch size s = n2/3 . They are chosen from by experimenting with {1, 0.1, 0.01, 0.001, 0.0001}, and η = 0.001 works best for VRSC-PG and for C-SAGA. For the case where S = 10, both VRSC-PG and C-SAGA use the same batch size s = 1. C-SAGA takes a step size η = 0.1, while VRSC-PG takes a stepsize of η = 0.03, because it diverges under η = 0.1 and η = 0.03 seems to work best VRSC-PG. For S = 100, we set η = 0.005 and batch size s = 10 for C-SAGA and VRSC-PG. The step size is chosen as the best among {0.1, 0.05, 0.01, 0.005, 0.001}. |