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 [1].
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Authors: Jacob Abernethy, Elad Hazan
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This mathematical equivalence is demonstrated in figure 3 generated by simulation over a polytope. |
| Researcher Affiliation | Academia | Jacob Abernethy EMAIL Computer Science & Engineering, University of Michigan Elad Hazan EMAIL Department of Computer Science, Princeton University |
| Pseudocode | Yes | Algorithm 1 HITANDRUN( , OK, N, , X0), Algorithm 2 SIMULATEDANNEALING WITH HITANDRUN Kalai & Vempala (2006), Algorithm 3 ITERATIVENEWTONSTEP |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes theoretical algorithms and mathematical equivalences, not experiments conducted on specific public datasets. Figure 3 is a simulation for illustration, not based on a publicly accessible training dataset. |
| Dataset Splits | No | The paper does not describe any specific training, validation, or test dataset splits, as it focuses on theoretical analysis and algorithmic properties rather than empirical data validation. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for computations or experiments. |
| Software Dependencies | No | The paper discusses algorithms and mathematical frameworks but does not specify any software dependencies (e.g., libraries, programming languages, or solvers) with version numbers. |
| Experiment Setup | No | The paper describes algorithmic details and theoretical parameters (e.g., temperature schedules) as part of its mathematical analysis, but it does not provide specific experimental setup details such as hyperparameters, optimization settings, or training configurations for an empirical study. |