| LDR | | 02038nam u200433 4500 |
| 001 | | 000000422306 |
| 005 | | 20190215170024 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438068841 |
| 035 | |
▼a (MiAaPQ)AAI10828041 |
| 035 | |
▼a (MiAaPQ)ucla:16939 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 519 |
| 100 | 1 |
▼a Chao, Hsiao-Han. |
| 245 | 10 |
▼a Structured Low-rank Matrix Approximation in Signal Processing: Semidefinite Formulations and Entropic First-order Methods. |
| 260 | |
▼a [S.l.]:
▼b University of California, Los Angeles.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 151 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Lieven Vandenberghe. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0031. |
| 650 | 4 |
▼a Applied mathematics. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer engineering. |
| 690 | |
▼a 0364 |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0464 |
| 710 | 20 |
▼a University of California, Los Angeles.
▼b Electrical Engineering 0303. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0031 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999106
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |