Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
Authors: Kimon Antonakopoulos, E. Veronica Belmega, Panayotis Mertikopoulos
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we supplement our theoretical analysis with numerical experiments in Poisson inverse problems. |
| Researcher Affiliation | Academia | Kimon Antonakopoulos Inria, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France kimon.antonakopoulos@inria.fr E. Veronica Belmega ETIS UMR8051, CY University, ENSEA, CNRS, F-95000, Cergy, France belmega@ensea.fr Panayotis Mertikopoulos Inria, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France panayotis.mertikopoulos@imag.fr |
| Pseudocode | No | The paper describes algorithms (FTRL and OMD) using mathematical formulations, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using a '384 × 384 test image contaminated with Poisson noise' for a 'Poisson denoising problem', implying a specific image (Lena test image is mentioned in the caption of Fig 1). However, it does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions 'minibatch size (n = 256)' and a 'test image' but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (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 (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | All algorithms were run with stochastic gradients drawn with the same minibatch size (n = 256) and a step-size of the form γt ∝ 1/√t. |