Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
Authors: Miguel Vaquero, Jorge Cortes
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 mivaquerovallina@ucsd.edu Jorge Cortés Mechanical and Aerospace Engineering UC San Diego San Diego, CA 9500 cortes@ucsd.edu |
| 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. |