Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Authors: Dongruo Zhou, Quanquan Gu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the lower bounds for smooth nonconvex finite-sum optimization, where the objective function is the average of n nonconvex component functions. We prove tight lower bounds for the complexity of finding -suboptimal point and -approximate stationary point in different settings, for a wide regime of the smallest eigenvalue of the Hessian of the objective function (or each component function). |
| Researcher Affiliation | Academia | Department of Computer Science, University of California, Los Angeles. Correspondence to: Quanquan Gu <qgu@cs.ucla.edu>. |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and lower bounds. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not present a new method with associated source code. Therefore, no information about open-source code for a new methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. Therefore, no information about datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets or their splits for experimental validation. Therefore, no information about validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve running experiments that would require specific software dependencies with version numbers. Therefore, no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments with specific setups, hyperparameters, or training configurations. Therefore, no experiment setup details are provided. |