Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements
Authors: Kimon Antonakopoulos, Panayotis Mertikopoulos
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Fig. 1, we report the performance of the (non-adaptive) entropic gradient descent and proportional response algorithms studied by Birnbaum et al. [10], and we compare it to the performance of ADAMIR, which consistently outperforms both methods, in terms of both last-iterate and ergodic value convergence rates. We provide a more detailed analysis in the paper s supplement. In the supplement, we also perform a numerical validation of the method in the context of a Fisher market model. |
| Researcher Affiliation | Collaboration | Kimon Antonakopoulos Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG 38000 Grenoble, France & Criteo AI Lab kimon.antonakopoulos@inria.fr panayotis.mertikopoulos@imag.fr |
| Pseudocode | No | The paper describes the ADAMIR method using mathematical equations and definitions but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper conducts a case study on 'Fisher markets' where 'marginal utilities drawn i.i.d. at each epoch' are used, implying a simulated environment. It does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes experiments on a simulated Fisher market but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In Fig. 1, we report the performance of the (non-adaptive) entropic gradient descent and proportional response algorithms... in a stochastic Fisher market with marginal utilities drawn i.i.d. at each epoch. The marked lines are the observed means from S = 50 realizations, whereas the shaded areas represent a 95% confidence interval. |