Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality
Authors: Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the above result empirically, we simulated δt in (6) throughout all iterations of Algorithms 1 for different sets of parameters and presented the results in Figure 1 along with their corresponding convergence rates given in Corollary 1. As it is shown in these figure, the above convergence rates correctly capture the behaviour of the dynamics in (6). |
| Researcher Affiliation | Academia | Ilyas Fatkhullin ETH AI Center & ETH Zurich Jalal Etesami* EPFL Niao He ETH Zurich Negar Kiyavash EPFL |
| Pseudocode | Yes | Algorithm 1: SGD with restarts ... Algorithm 2: PAGER (PAGE with restarts) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code. |
| Open Datasets | No | The paper focuses on theoretical analysis and simulations of dynamics, not empirical evaluation on specific, publicly available datasets. No dataset names or access information are provided. |
| Dataset Splits | No | The paper does not describe experiments on datasets with specified training/test/validation splits. It refers to simulations of theoretical dynamics. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for any computational work or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper describes parameters used for simulating theoretical dynamics (e.g., h(t) = tβ, ϕ(t) = 2µ t1/α, τ values for Figure 1) but does not provide specific experimental setup details like hyperparameters, optimizers, or training configurations for machine learning models or real-world data experiments. |