상세정보
Export to Refworks부가기능
Structured Low-rank Matrix Approximation in Signal Processing: Semidefinite Formulations and Entropic First-order Methods
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Structured Low-rank Matrix Approximation in Signal Processing: Semidefinite Formulations and Entropic First-order Methods. |
| 개인저자 | Chao, Hsiao-Han. |
| 단체저자명 | University of California, Los Angeles. Electrical Engineering 0303. |
| 발행사항 | [S.l.]: University of California, Los Angeles., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 151 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438068841 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Lieven Vandenberghe. |
| 요약 | Applications of semidefinite optimization in signal processing are often derived from the Kalman--Yakubovich--Popov lemma and its extensions, which give sum-of-squares theorems of nonnegative trigonometric polynomials and generalized polynomials |
| 요약 | The thesis can be divided into two parts. As a first contribution, we extend the semidefinite penalty formulations in super-resolution applications to more general types of structured low-rank matrix approximations. The penalty functions for str |
| 일반주제명 | Applied mathematics. Electrical engineering. Computer engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
