Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems
Authors: Ting-Jui Chang, Shahin Shahrampour6966-6973
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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 . |