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..
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
Authors: Miguel Vaquero, Jorge Cortes
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various simulations show the superior performance of the proposed method in comparison with recently proposed constant-stepsize discretizations. |
| Researcher Affiliation | Academia | Miguel Vaquero Mechanical and Aerospace Engineering UC San Diego San Diego, CA 9500 EMAIL Jorge Cortés Mechanical and Aerospace Engineering UC San Diego San Diego, CA 9500 EMAIL |
| Pseudocode | Yes | Algorithm 1 describes in pseudocode the resulting variable-stepsize integrator. |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The objective function corresponds to the regularized logistic regression cost function, namely P10 i=1 log(1 + e yi vi,x ) + 1/2 x 2, where x R4 and we have generated the sampled points (vi, yi) randomly. This function is 1-strongly convex. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It mentions generating data randomly or using a quadratic objective function. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set α = µ/4 and s = µ/(36L2) following the values in [24]. The objective function corresponds to the regularized logistic regression cost function, namely P10 i=1 log(1 + e yi vi,x ) + 1/2 x 2, where x R4 and we have generated the sampled points (vi, yi) randomly. |