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..
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Authors: Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamas Keviczky
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, a numerical example is given. ... We consider a quadratic objective function f(x1) = x 1 Qx1 where x1 R5 with the matrix Q = diag{0.1, 0.2, , 0.5}. ... Figure 1 reports the performance of NSR for two values kmin {1, 6, 7}. We note that when kmin = 6, 7, NSR is no longer monotone, while it remains monotone for kmin 5. |
| Researcher Affiliation | Academia | Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Correspondence to: Arman Sharifi Kolarijani <a.sharifikolarijani@tudelft.nl>. |
| Pseudocode | Yes | Algorithm 1 Sate Dependent Scheme Input: data x0 1, ℓf, Lf, µf, α R+, kmax N+ Set: c1 = c2 = β 1 = Lfs, x0 2 = β f(x0 1) x0 = (x0 1, x0 2) for k = 1 to kmax do if c1 xk 2 2 f(xk 1) 2 c2 f(xk 1), xk 2 then xk+1 Fd(xk) else xk+1 Gd(xk) end if end for |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses a synthetic quadratic objective function for its numerical example, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper uses a synthetic quadratic objective function for its numerical example, and therefore does not specify train/validation/test dataset splits. |
| Hardware Specification | No | The paper describes a numerical example but does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions comparing performance with 'Nesterov’s accelerated method' and 'Algorithm 1', but does not provide specific software versions or dependencies used for implementation. |
| Experiment Setup | Yes | In what follows, we compare the performance of Algorithm 1 (denoted by HD) with that of Nesterov s accelerated method using the speed restarting scheme proposed in (Su et al., 2016) (denoted by NSR). We set s = 1/Lf in Algorithm 1 and the rest of the parameters are computed according to Corollary 3.9. The NSR algorithm requires a tuning parameter kmin that is the minimum number of iterations between two consecutive restart instants. |