Topics in convex optimization for machine learning
Abstract/Contents
- Abstract
- Convex optimization has been well-studied as a mathematical topic for more than a century, and has been applied in practice in many application areas for about a half century in fields including control, finance, signal processing, data mining, and machine learning. This thesis focuses on several topics involving convex optimization, with the specific application of machine learning
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Park, Youngsuk |
|---|---|
| Degree supervisor | Boyd, Stephen P |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Weissman, Tsachy |
| Degree committee member | Leskovec, Jurij |
| Degree committee member | Weissman, Tsachy |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Youngsuk Park |
|---|---|
| Note | Submitted to the Department of Electrical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Youngsuk Park
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...