Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function
Authors: Quoc Tran Dinh, Deyi Liu, Lam Nguyen
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the benefits of our algorithms over existing methods through two numerical examples, including a nonsmooth and nonconvex-non-strongly concave minimax model. We use two examples to illustrate our algorithm and compare it with existing methods. The performance of 3 algorithms are shown in Figure 1 for three datasets using b := N/8 (8 blocks). |
| Researcher Affiliation | Collaboration | Department of Statistics and Operations Research The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 Emails: {quoctd@email.unc.edu, deyi.liu@live.unc.edu} IBM Research, Thomas J. Watson Research Center Yorktown Heights, NY10598, USA. Email: lamnguyen.mltd@ibm.com |
| Pseudocode | Yes | Algorithm 1 (Smoothing Hybrid Variance-Reduced SGD Algorithm for solving (1)) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We test it on three real-world portfolio datasets, which contain 29, 37, and 47 portfolios, respectively, from the Keneth R. French Data Library [1]. We test them on 3 datasets from LIBSVM [6]. |
| Dataset Splits | No | The paper uses datasets from Keneth R. French Data Library and LIBSVM but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | Our code is implemented in Python 3.6.3, running on a Linux desktop (3.6GHz Intel Core i7 and 16Gb memory). |
| Software Dependencies | Yes | Our code is implemented in Python 3.6.3 |
| Experiment Setup | Yes | We set ρ := 0.2 and λ := 0.01 as in [44]... The step-size η of all algorithms are well tuned from a set of trials {1, 0.5, 0.1, 0.05, 0.01, 0.001, 0.0001}. We set λ := 10 4 and update our γt parameter as γt := 1 2(t+1)1/3 . The step-size η of all algorithms are well tuned from {1, 0.5, 0.1, 0.05, 0.01, 0.001, 0.0001}... |