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..
Finite-Time Convergence in Continuous-Time Optimization
Authors: Orlando Romero, Mouhacine Benosman
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conducted some numerical experiments to illustrate our results. In this section, we illustrate the finite-time convergence properties of the q-RGF (19) and our designed second-order flow (27) on academic optimization test functions. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA. 2Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that source code for the methodology is available. |
| Open Datasets | No | The paper uses a synthetically generated dataset based on a log-sum-exp function with parameters sampled from a N(0,1) distribution, but does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | First, we fix x0 = 3/4 and vary q > 1. The results are reported in Figure 1. [...] Next, we fix q = 10 and vary x0 R near x = 0, while maintaining every other parameter the same as before. [...] We now test the second-order flow (27) with (c, α, r) = ( f(x0) , 1/2, 1) on the optimization testbed function known as the Rosenbrock function, namely f : R2 R given by f(x1, x2) = (a x1)2 + b(x2 x2 1)2, with parameters a, b R. |