Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes
Authors: Daniil Vankov, Angelia Nedich, Lalitha Sankar
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments that support our theoretical guarantees and highlight the efficiency of proposed adaptive stepsizes. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Arizona State University Tempe, Arizona, USA. |
| Pseudocode | Yes | Algorithm 1 Korpelevich Method with Backtracking |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | We present the experiments on training GAN for the 2D Ring dataset, a mixture of 8 equal-prior Gaussian distributions, with mean cos(2πi/8), sin(2πi/8) for i {1, . . . , 8} and variance 10 4. The paper describes how the dataset was generated but does not provide access information (link, DOI, formal citation) to a publicly available version of this dataset. |
| Dataset Splits | No | The paper mentions running methods for 100 epochs with a batch size of 128 but does not provide explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory specifications, or types of computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | We set parameters of the problem to be {(α 0.090., p = 2.1), (α 0.66, p = 4.0), (α 0.86, p = 8.0)}. We run methods for 100 epochs with a batch size of 128. γk = 10 3 for methods without clipping, and γk = 10 3 min{1, 1 F (hk) } for methods with clipping. Korpelevich method with clipping and backtracking (Algorithm 1) with β = 1.0 and q = 0.75. γk = βk min{1, 1 F (hk) , 1 ( uk hk 1 +1)α/(1 α) }, with the same βk = a b+k for all methods, where a = b = 100. |