Tight Complexity Bounds for Optimizing Composite Objectives
Authors: Blake E. Woodworth, Nati Srebro
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide tight upper and lower bounds on the complexity of minimizing the average of m convex functions using gradient and prox oracles of the component functions. |
| Researcher Affiliation | Academia | Blake Woodworth Toyota Technological Institute at Chicago Chicago, IL, 60637 blake@ttic.edu Nathan Srebro Toyota Technological Institute at Chicago Chicago, IL, 60637 nati@ttic.edu |
| Pseudocode | No | The paper describes various algorithms and methods (e.g., AGD, SVRG, SAG, KATYUSHA) conceptually, but it does not provide any structured pseudocode or algorithm blocks for them. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code for the described methods, nor does it include links to any code repositories. |
| Open Datasets | No | This paper is theoretical and does not describe any empirical studies or the use of datasets. |
| Dataset Splits | No | This paper is theoretical and does not describe any empirical studies or the use of datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | This paper is theoretical and does not report on any experiments requiring specific hardware; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not report on any software implementations or dependencies; therefore, no specific software version numbers are provided. |
| Experiment Setup | No | This paper is theoretical and does not describe any conducted experiments; therefore, no experimental setup details such as hyperparameters or training configurations are provided. |