Iterative methods for structured algorithmic data science
Abstract/Contents
- Abstract
- This thesis studies the interplay between two aspects of algorithmic data science: iterative method theory and structured problem instances arising from applications. We organize the thesis into two parts, each of which couples a branch of the modern iterative method toolkit with a family of related structured problems in data science. Each part of the thesis develops new frameworks for designing iterative methods, gives new tools for the efficient implementation of said methods, and offers fresh perspectives on the relationships between our new iterative methods and various fundamental algorithmic problems in the relevant application domain.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tian, Kevin Jimaine |
|---|---|
| Degree supervisor | Sidford, Aaron |
| Thesis advisor | Sidford, Aaron |
| Thesis advisor | Ahmadipouranari, Nima |
| Thesis advisor | Charikar, Moses |
| Thesis advisor | Valiant, Gregory |
| Degree committee member | Ahmadipouranari, Nima |
| Degree committee member | Charikar, Moses |
| Degree committee member | Valiant, Gregory |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Kevin Tian. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/qy752rq1707 |
Access conditions
- Copyright
- © 2022 by Kevin Jimaine Tian
Also listed in
Loading usage metrics...