A Lower Bound for the Optimization of Finite Sums
Authors: Alekh Agarwal, Leon Bottou
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a lower bound for optimizing a finite sum of n functions, where each function is L-smooth and the sum is µ-strongly convex. |
| Researcher Affiliation | Industry | Alekh Agarwal ALEKHA@MICROSOFT.COM Microsoft Research NYC, New York, NY. Léon Bottou LEON@BOTTOU.ORG Facebook AI Research, New York, NY. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on lower bounds for optimization. It does not conduct experiments with datasets, and therefore no information about publicly available training data is provided. |
| Dataset Splits | No | This is a theoretical paper focused on lower bounds for optimization. It does not conduct experiments with datasets, and therefore no information about validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or system-level training settings. |