A Theoretical Analysis of Optimization by Gaussian Continuation
Authors: Hossein Mobahi, John Fisher III
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we provide a theoretical analysis that provides a bound on the endpoint solution of the continuation method. The derived bound depends on a problem specific characteristic that we refer to as optimization complexity. We show that this characteristic can be analytically computed when the objective function is expressed in some suitable basis functions. Our analysis combines elements of scale-space theory, regularization and differential equations. |
| Researcher Affiliation | Academia | Hossein Mobahi and John W. Fisher III Computer Science and Artificial Intelligence Lab. (CSAIL) Massachusetts Institute of Technology (MIT) hmobahi,fisher@csail.mit.edu |
| Pseudocode | Yes | Algorithm 1 Algorithm for Optimization by Continuation Method |
| Open Source Code | No | The paper is theoretical and focuses on mathematical analysis. It does not mention releasing any source code for its theoretical contributions or analysis methods. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies or use datasets. Therefore, it does not provide information about public datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or data. Therefore, it does not specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments. Therefore, no hardware specifications are mentioned or required. |
| Software Dependencies | No | The paper is purely theoretical and focuses on mathematical derivations. It does not involve software implementation or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments. Therefore, it does not provide details on experimental setup, hyperparameters, or training configurations. |