On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems
Authors: Ting-Jui Chang, Shahin Shahrampour6966-6973
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Fig. 1, we can see that the regret incurred using the stale function information grows much faster than the one using the perfectly-predicted information, which verifies the theoretical advantage of OON. |
| Researcher Affiliation | Academia | Ting-Jui Chang, Shahin Shahrampour Wm Michael Barnes 64 Department of Industrial and Systems Engineering Texas A&M University, College Station, TX, USA {tingjui.chang, shahin}@tamu.edu |
| Pseudocode | Yes | Algorithm 1 Online Preconditioned Gradient Descent (OPGD); Algorithm 2 Optimistic Online Newton (OON); Algorithm 3 Online Multiple Gradient Descent (OMGD) (Zhang et al. 2017); Algorithm 4 Online Preconditioned Gradient Descent (OPGD) for Constrained Setup; Algorithm 5 Online Multiple Gradient Descent (OMGD) (Zhang et al. 2017) for Constrained Setup |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a synthetic function sequence generated for experiments but does not provide access information (link, DOI, repository, or citation) for a publicly available dataset. |
| Dataset Splits | No | The paper conducts experiments on a synthetically generated function sequence but does not mention dataset splits (e.g., train/validation/test percentages or counts) as it's not a standard dataset evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) 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) needed to replicate the experiment. |
| Experiment Setup | Yes | Consider a function sequence of the following form ft(x) = (x x t ) Qt(x x t ) 2+ 1 2(x x t ) Qt(x x t ), where Qt is a positive definite matrix and αI Qt βI (α = 1 and β = 30). ... The optimal point of the next function is randomly selected from the sphere centered at the current optimal point with radius α LH . |