| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434965 |
| 005 | | 20200227112651 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687934871 |
| 035 | |
▼a (MiAaPQ)AAI27536305 |
| 035 | |
▼a (MiAaPQ)umichrackham002317 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 620 |
| 100 | 1 |
▼a Fuentes, Victor K. |
| 245 | 10 |
▼a On Computing Sparse Generalized Inverses and Sparse-Inverse/Low-Rank Decompositions. |
| 260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 115 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: B. |
| 500 | |
▼a Advisor: Lee, Jon. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a Pseudoinverses are ubiquitous tools for handling over- and under-determined systems of equations. For computational efficiency, sparse pseudoinverses are desirable. Recently, sparse left and right pseudoinverses were introduced, using 1-norm minimization and linear programming. We introduce several new sparse generalized inverses by using 1-norm minimization on a subset of the linear Moore-Penrose properties, again leading to linear programming. Computationally, we demonstrate the usefulness of our approach in the context of application to least-squares problems and minimum 2-norm problems. One of the Moore-Penrose properties is nonlinear (in fact, quadratic), and so developing an effective convex relaxation for it is nontrivial. We develop a variety of methods for this, in particular a nonsymmetric lifting which is more efficient than the usual symmetric lifting that is normally applied to non-convex quadratic equations. In this context, we develop a novel and computationally effective "diving procedure" to find a path of solutions trading off sparsity against the nice properties of the Moore- Penrose pseudoinverse. Next, we consider the well-known low-rank/sparse decomposition problemmin {. |
| 590 | |
▼a School code: 0127. |
| 650 | 4 |
▼a Operations research. |
| 650 | 4 |
▼a Mathematics. |
| 650 | 4 |
▼a Engineering. |
| 690 | |
▼a 0796 |
| 690 | |
▼a 0537 |
| 690 | |
▼a 0405 |
| 710 | 20 |
▼a University of Michigan.
▼b Industrial & Operations Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-05B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0127 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494249
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |