Finite-Time Convergence in Continuous-Time Optimization
Authors: Orlando Romero, Mouhacine Benosman
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |